SlideShare a Scribd company logo

Deep Convolutional GANs - meaning of latent space

•
•
Hansol Kang
Hansol Kang

DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음. DCGAN 논문 리뷰 및 PyTorch 기반의 구현. VAE 세미나 이슈 사항에 대한 리뷰. my github : https://github.com/messy-snail/GAN_PyTorch [참고] https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN https://github.com/taeoh-kim/Pytorch_DCGAN Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).

Technology
1 of 43
Download to read offline
Deep Convolutional GANs ISL Lab Seminar Hansol Kang : Meaning of Latent Space
Contents Review of GAN DCGAN Experiment Summary 2018-10-05 2
Review of GAN 2018-10-05 3 • Adversarial nets 1) Global Optimality of datag pp  2) Convergence of Algorithm D GVs x )(xpdata “Generative Adversarial Networks” Goal Method
D DCGAN 2018-10-05 4 • Introduction * Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015). * “I have the strongest MLP army.” “I have too.” G
DCGAN 2018-10-05 6 • Introduction * D G “What are they doing?” “We have a better CNN than MLP” D “I have the strongest MLP army.” “I have too.” G Vanilla GAN DCGAN * Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
DCGAN 2018-10-05 7 • Contributions Generating Natural Image Deep Convolutional GANs Image Classification using D Filter Visualization Vector arithmetic properties
Z“I’m very Important” Who am I? Black box Real D A B C DCGAN 2018-10-05 8 • Contributions Generating Natural Image Deep Convolutional GANs Image Classification using D Filter Visualization Vector arithmetic properties
DCGAN 2018-10-05 9 • Approach and Model Architecture Replace any pooling layers with strided convolutions (discriminator) and fractional- strided convolutions (generator). Use batchnorm in both the generator and the discriminator. Remove fully connected hidden layers for deeper architectures. Use ReLU activation in generator for all layers except for the output, which uses Tanh. Use LeakyReLU activation in the discriminator for all layers.
DCGAN 2018-10-05 10 • Approach and Model Architecture Strided Convolution Fractional Convolution(Transposed Convolution)
DCGAN 2018-10-05 11 • Approach and Model Architecture Batch Normalization Except for these layers. Output layer of Generator Input layer of Discriminator
DCGAN 2018-10-05 12 • Approach and Model Architecture No fully connected layer Classical CNN GAP(Global Average Pooling) http://nmhkahn.github.io/Casestudy-CNN
DCGAN 2018-10-05 13 • Approach and Model Architecture No fully connected layer https://raw.githubusercontent.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN/master/pytorch_DCGAN.png
DCGAN 2018-10-05 14 • Approach and Model Architecture ReLU, Tanh, LeakyReLU http://gmelli.org/RKB/Rectified_Linear_Unit_(ReLU)_Activation_Function Generator : ReLU, Tanh Discriminator : LeakyReLu , Sigmoid
DCGAN 2018-10-05 15 • Details of Adversarial Training • Mini-batch stochastic gradient descent(SGD); mini-batch size of 128 • All weights initialized from a zero-centered Normal distribution with standard deviation 0.02 • Leaky slope 0.2 • Adam optimizer; lr =0.0002, beta1 = 0.9, beta2 = 0.5
DCGAN 2018-10-05 16 • Details of Adversarial Training LSUN dataset 1 epoch
DCGAN 2018-10-05 17 • Details of Adversarial Training LSUN dataset 5 epochs
DCGAN 2018-10-05 18 • Empirical Validation of DCGANs Capabilities • CIFAR-10 • Classification • Domain robustness
DCGAN 2018-10-05 19 • Empirical Validation of DCGANs Capabilities SVHN(Street View House Numbers) dataset
DCGAN 2018-10-05 20 • Investigating and Visualizing The Internals of The Networks Walking in the latent space
DCGAN 2018-10-05 21 • Investigating and Visualizing The Internals of The Networks(cont.) Visualizing the discriminator features
DCGAN 2018-10-05 22 • Investigating and Visualizing The Internals of The Networks(cont.) Forgetting to draw certain objects in charge of windows in charge of beds in charge of lamps in charge of doors … Latent code Filters(Conv) Generation 1 0 0 Noise(z)
DCGAN 2018-10-05 23 • Investigating and Visualizing The Internals of The Networks(cont.) Forgetting to draw certain objects
DCGAN 2018-10-05 24 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
DCGAN 2018-10-05 25 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
DCGAN 2018-10-05 26 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
DCGAN 2018-10-05 27 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
Experiment • Code 2018-10-05 28https://github.com/messy-snail/GAN_PyTorch
Experiment • Code 2018-10-05 29https://github.com/messy-snail/GAN_PyTorch
Experiment • Results#1 CelebA 2018-10-05 30 Ground Truth Vanilla GAN : DCGAN : Epoch 1 Epoch 5 Epoch 100 Epoch 1 Epoch 5 Epoch 30 Still have this sample Results are cherry picked
Experiment • Results#2 LSUN) 2018-10-05 31 Ground Truth Vanilla GAN : DCGAN : Epoch 1 Epoch 5 Epoch 12 Epoch 1 Epoch 2 Epoch 5 Results are cherry picked
Experiment • Results#3 Korean Idol – Transfer trial 2018-10-05 32 • I used weights and biases generated by celebA learning. • I wanted the effect of transfer learning but failed. Maybe these factors (Asian, cropping image) Ground Truth Epoch 1 Epoch 2 Epoch 3 Epoch 4 Epoch 5 Epoch 6
Experiment • Results#4 Korean Idol 2018-10-05 33 Ground Truth Epoch 1 Epoch 5 Epoch 30 Epoch 50 Epoch 100 Epoch 150 • 10000 images Insufficient data set
Summary 2018-10-05 34 • Stable set of architectures for training generative adversarial networks • Good representations of images for supervised learning and generative modeling • Sometimes collapse a subset of filters to a single oscillating mode • Latent code has a special meaning, not a simple noise component. [Instability of GAN]
Future work 2018-10-05 35 Paper Review Vanilla GAN DCGAN InfoGAN Unrolled GAN Wasserstein GAN LS GAN BEGAN Pix2Pix Cycle GAN Proposed Model SpyGAN Tips Document Programming Mathematical Study Information theory (working title)
&
Appendix • Issues at the VAE Seminar (18.07.23) 2018-10-05 37  Issue#1 Performance of VAE and GAN  Issue#2 Log likelihood  Issue#3 Dimension of latent code  Issue#4 Why manifold? Durk Kingma 1. Adam: A Method for Stochastic Optimization 2. Auto-Encoding Variational Bayes Machine Learning researcher at OpenAI - Mathematically very difficult papers Intuitive explanation https://www.youtube.com/watch?v=o_peo6U7IRM 오토인코더의 모든 것 : I refer to this video
Appendix • Issue #1 Performance of VAE and GAN 2018-10-05 38 “Compared to GAN, VAE is relatively blurred and I do not know why.” “Cost function”     )(||)|()(|log),,( )|( zpxzqKLzgxExL xzq    ),,(min xL VAE ),(maxmin DGV DG       zGDExDEDGV zdata pzpx  1log)(log),( ~~ GAN Intuition Reconstruction Error Regularization ≈ D Loss ≈ G Loss
Appendix • Issue #1 Performance of VAE and GAN 2018-10-05 39 VAE Loss= Recon. Error + Regularization GAN Loss= G_Loss + D_Loss E D Recon. Error D Real Fake 1. Optimize 2. Image Quality 3. Generalization VAE vs. GAN
Appendix • Issue #2 Log likelihood 2018-10-05 40 Question about log likelihood “Summation and monotonically increasing” MLE(Maximum Likelihood Estimation) : Unknown parameter estimation from observation )|(maxargˆ   yp eg. Gaussian Distribution Mean and Std  i iyp )|(maxarg          i i i i ypyp )|(logmaxarg)|(logmaxarg   : monotonically increasing function1 Log(x) cf.  i ixp )(logmaxarg   Generation model
Appendix • Issue #3 Dimension of latent code 2018-10-05 41 “Is the latent code dimension always small?” “Yes” AE, What’s this? Dimension reduction E D High Low Interested Sparse AE FAILED
Appendix • Issue #4 Why manifold? 2018-10-05 42 What’s the manifold and Why explain the manifold? “Concept of manifold and Difference of between AE and VAE” High Low Subspace =Manifold Concept of manifold D Purpose of AE : Manifold Learning Purpose of AE and VAE Assumption(manifold hypothesis) Uniform sampling E Unsupervised Learning D Purpose of VAE : Generative Model E Unsupervised Learning : Correlation between generation and manifold…
Appendix • PyTorch (Variable length inputs) 2018-10-05 43 Shape = {Size} torch.Size([128, 3, 32, 32]) Shape = {Size} torch.Size([128, 64, 16, 16]) Shape = {Size} torch.Size([128, 16384])3x32x32 CIFAR-10 Shape = {Size} torch.Size([128, 64, 109, 89]) Shape = {Size} torch.Size([128, 3, 218, 178]) Shape = {Size} torch.Size([128, 620864]) 3x178x218 CelebA Conv Input Pool FC Conv2d(in_ch, out_ch, k_size, s, p) Reshape(bat_sz,-1) Input size is not fixed.

Recommended

딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)
딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)
딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)
Hansol Kang
 
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)쉽게 설명하는 GAN (What is this? Gum? It's GAN.)
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)
Hansol Kang
 
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...Photo-realistic Single Image Super-resolution using a Generative Adversarial ...
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...
Hansol Kang
 
PyTorch 튜토리얼 (Touch to PyTorch)
PyTorch 튜토리얼 (Touch to PyTorch)PyTorch 튜토리얼 (Touch to PyTorch)
PyTorch 튜토리얼 (Touch to PyTorch)
Hansol Kang
 
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)
Hansol Kang
 
Automatic Features Generation And Model Training On Spark: A Bayesian Approach
Automatic Features Generation And Model Training On Spark: A Bayesian ApproachAutomatic Features Generation And Model Training On Spark: A Bayesian Approach
Automatic Features Generation And Model Training On Spark: A Bayesian Approach
Spark Summit
 
強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷
Eiji Sekiya
 
CppConcurrencyInAction - Chapter07
CppConcurrencyInAction - Chapter07CppConcurrencyInAction - Chapter07
CppConcurrencyInAction - Chapter07
DooSeon Choi
 

Recommended

딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)
딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)
딥러닝 중급 - AlexNet과 VggNet (Basic of DCNN : AlexNet and VggNet)
Hansol Kang
 
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)쉽게 설명하는 GAN (What is this? Gum? It's GAN.)
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)
Hansol Kang
 
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...Photo-realistic Single Image Super-resolution using a Generative Adversarial ...
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...
Hansol Kang
 
PyTorch 튜토리얼 (Touch to PyTorch)
PyTorch 튜토리얼 (Touch to PyTorch)PyTorch 튜토리얼 (Touch to PyTorch)
PyTorch 튜토리얼 (Touch to PyTorch)
Hansol Kang
 
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)
Hansol Kang
 
Automatic Features Generation And Model Training On Spark: A Bayesian Approach
Automatic Features Generation And Model Training On Spark: A Bayesian ApproachAutomatic Features Generation And Model Training On Spark: A Bayesian Approach
Automatic Features Generation And Model Training On Spark: A Bayesian Approach
Spark Summit
 
強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷
Eiji Sekiya
 
CppConcurrencyInAction - Chapter07
CppConcurrencyInAction - Chapter07CppConcurrencyInAction - Chapter07
CppConcurrencyInAction - Chapter07
DooSeon Choi
 
Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Spark Summit
 
Scaling out logistic regression with Spark
Scaling out logistic regression with SparkScaling out logistic regression with Spark
Scaling out logistic regression with Spark
Barak Gitsis
 
A Walk in the GAN Zoo
A Walk in the GAN ZooA Walk in the GAN Zoo
A Walk in the GAN Zoo
Larry Guo
 
GeoMesa on Apache Spark SQL with Anthony Fox
GeoMesa on Apache Spark SQL with Anthony FoxGeoMesa on Apache Spark SQL with Anthony Fox
GeoMesa on Apache Spark SQL with Anthony Fox
Databricks
 
Time-Evolving Graph Processing On Commodity Clusters
Time-Evolving Graph Processing On Commodity ClustersTime-Evolving Graph Processing On Commodity Clusters
Time-Evolving Graph Processing On Commodity Clusters
Jen Aman
 
Scaling up data science applications
Scaling up data science applicationsScaling up data science applications
Scaling up data science applications
Kexin Xie
 
Multimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-AnsweringMultimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-Answering
NAVER D2
 
Spark algorithms
Spark algorithmsSpark algorithms
Spark algorithms
Ashutosh Trivedi
 
Selection analysis using HyPhy
Selection analysis using HyPhySelection analysis using HyPhy
Selection analysis using HyPhy
Bioinformatics and Computational Biosciences Branch
 
Realtime Analytics
Realtime AnalyticsRealtime Analytics
Realtime Analytics
eXascale Infolab
 
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
Thom Lane
 
R and cpp
R and cppR and cpp
R and cpp
Romain Francois
 
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
PingCAP
 
Machine(s) Learning with Neural Networks
Machine(s) Learning with Neural NetworksMachine(s) Learning with Neural Networks
Machine(s) Learning with Neural Networks
Bruno Gonçalves
 
Quantum Gaussian Processes - Gawel Kus
Quantum Gaussian Processes - Gawel KusQuantum Gaussian Processes - Gawel Kus
Quantum Gaussian Processes - Gawel Kus
Advanced-Concepts-Team
 
A practical Introduction to Machine(s) Learning
A practical Introduction to Machine(s) LearningA practical Introduction to Machine(s) Learning
A practical Introduction to Machine(s) Learning
Bruno Gonçalves
 
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
台灣資料科學年會
 
Tuning the g1gc
Tuning the g1gcTuning the g1gc
Tuning the g1gc
Kirk Pepperdine
 
[241]large scale search with polysemous codes
[241]large scale search with polysemous codes[241]large scale search with polysemous codes
[241]large scale search with polysemous codes
NAVER D2
 
Let's Get to the Rapids
Let's Get to the RapidsLet's Get to the Rapids
Let's Get to the Rapids
Maurice Naftalin
 
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
MLconf
 
IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」
Preferred Networks
 

More Related Content

What's hot

Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Spark Summit
 
Scaling out logistic regression with Spark
Scaling out logistic regression with SparkScaling out logistic regression with Spark
Scaling out logistic regression with Spark
Barak Gitsis
 
A Walk in the GAN Zoo
A Walk in the GAN ZooA Walk in the GAN Zoo
A Walk in the GAN Zoo
Larry Guo
 
GeoMesa on Apache Spark SQL with Anthony Fox
GeoMesa on Apache Spark SQL with Anthony FoxGeoMesa on Apache Spark SQL with Anthony Fox
GeoMesa on Apache Spark SQL with Anthony Fox
Databricks
 
Time-Evolving Graph Processing On Commodity Clusters
Time-Evolving Graph Processing On Commodity ClustersTime-Evolving Graph Processing On Commodity Clusters
Time-Evolving Graph Processing On Commodity Clusters
Jen Aman
 
Scaling up data science applications
Scaling up data science applicationsScaling up data science applications
Scaling up data science applications
Kexin Xie
 
Multimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-AnsweringMultimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-Answering
NAVER D2
 
Spark algorithms
Spark algorithmsSpark algorithms
Spark algorithms
Ashutosh Trivedi
 
Selection analysis using HyPhy
Selection analysis using HyPhySelection analysis using HyPhy
Selection analysis using HyPhy
Bioinformatics and Computational Biosciences Branch
 
Realtime Analytics
Realtime AnalyticsRealtime Analytics
Realtime Analytics
eXascale Infolab
 
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
Thom Lane
 
R and cpp
R and cppR and cpp
R and cpp
Romain Francois
 
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
PingCAP
 
Machine(s) Learning with Neural Networks
Machine(s) Learning with Neural NetworksMachine(s) Learning with Neural Networks
Machine(s) Learning with Neural Networks
Bruno Gonçalves
 
Quantum Gaussian Processes - Gawel Kus
Quantum Gaussian Processes - Gawel KusQuantum Gaussian Processes - Gawel Kus
Quantum Gaussian Processes - Gawel Kus
Advanced-Concepts-Team
 
A practical Introduction to Machine(s) Learning
A practical Introduction to Machine(s) LearningA practical Introduction to Machine(s) Learning
A practical Introduction to Machine(s) Learning
Bruno Gonçalves
 
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
台灣資料科學年會
 
Tuning the g1gc
Tuning the g1gcTuning the g1gc
Tuning the g1gc
Kirk Pepperdine
 
[241]large scale search with polysemous codes
[241]large scale search with polysemous codes[241]large scale search with polysemous codes
[241]large scale search with polysemous codes
NAVER D2
 
Let's Get to the Rapids
Let's Get to the RapidsLet's Get to the Rapids
Let's Get to the Rapids
Maurice Naftalin
 

What's hot (20)

Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
Engineering Fast Indexes for Big-Data Applications: Spark Summit East talk by...
 
Scaling out logistic regression with Spark
Scaling out logistic regression with SparkScaling out logistic regression with Spark
Scaling out logistic regression with Spark
 
A Walk in the GAN Zoo
A Walk in the GAN ZooA Walk in the GAN Zoo
A Walk in the GAN Zoo
 
GeoMesa on Apache Spark SQL with Anthony Fox
GeoMesa on Apache Spark SQL with Anthony FoxGeoMesa on Apache Spark SQL with Anthony Fox
GeoMesa on Apache Spark SQL with Anthony Fox
 
Time-Evolving Graph Processing On Commodity Clusters
Time-Evolving Graph Processing On Commodity ClustersTime-Evolving Graph Processing On Commodity Clusters
Time-Evolving Graph Processing On Commodity Clusters
 
Scaling up data science applications
Scaling up data science applicationsScaling up data science applications
Scaling up data science applications
 
Multimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-AnsweringMultimodal Residual Learning for Visual Question-Answering
Multimodal Residual Learning for Visual Question-Answering
 
Spark algorithms
Spark algorithmsSpark algorithms
Spark algorithms
 
Selection analysis using HyPhy
Selection analysis using HyPhySelection analysis using HyPhy
Selection analysis using HyPhy
 
Realtime Analytics
Realtime AnalyticsRealtime Analytics
Realtime Analytics
 
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
CIFAR-10 for DAWNBench: Wide ResNets, Mixup Augmentation and "Super Convergen...
 
R and cpp
R and cppR and cpp
R and cpp
 
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
[Paper Reading] Generalized Sub-Query Fusion for Eliminating Redundant I/O fr...
 
Machine(s) Learning with Neural Networks
Machine(s) Learning with Neural NetworksMachine(s) Learning with Neural Networks
Machine(s) Learning with Neural Networks
 
Quantum Gaussian Processes - Gawel Kus
Quantum Gaussian Processes - Gawel KusQuantum Gaussian Processes - Gawel Kus
Quantum Gaussian Processes - Gawel Kus
 
A practical Introduction to Machine(s) Learning
A practical Introduction to Machine(s) LearningA practical Introduction to Machine(s) Learning
A practical Introduction to Machine(s) Learning
 
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
[DSC 2016] 系列活動：李泳泉 / 星火燎原 - Spark 機器學習初探
 
Tuning the g1gc
Tuning the g1gcTuning the g1gc
Tuning the g1gc
 
[241]large scale search with polysemous codes
[241]large scale search with polysemous codes[241]large scale search with polysemous codes
[241]large scale search with polysemous codes
 
Let's Get to the Rapids
Let's Get to the RapidsLet's Get to the Rapids
Let's Get to the Rapids
 

Similar to Deep Convolutional GANs - meaning of latent space

Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
MLconf
 
IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」
Preferred Networks
 
Deep Learning in Computer Vision
Deep Learning in Computer VisionDeep Learning in Computer Vision
Deep Learning in Computer Vision
Sungjoon Choi
 
# Can we trust ai. the dilemma of model adjustment
# Can we trust ai. the dilemma of model adjustment# Can we trust ai. the dilemma of model adjustment
# Can we trust ai. the dilemma of model adjustment
Terence Huang
 
Implementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on SparkImplementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on Spark
Dalei Li
 
Introduction to Genetic algorithm and its significance in VLSI design and aut...
Introduction to Genetic algorithm and its significance in VLSI design and aut...Introduction to Genetic algorithm and its significance in VLSI design and aut...
Introduction to Genetic algorithm and its significance in VLSI design and aut...
Centre for Electronics, Computer, Self development
 
Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsUsing Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning Models
Scott Clark
 
Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsUsing Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning Models
SigOpt
 
MLSEV. Logistic Regression, Deepnets, and Time Series
MLSEV. Logistic Regression, Deepnets, and Time Series MLSEV. Logistic Regression, Deepnets, and Time Series
MLSEV. Logistic Regression, Deepnets, and Time Series
BigML, Inc
 
BSSML17 - Deepnets
BSSML17 - DeepnetsBSSML17 - Deepnets
BSSML17 - Deepnets
BigML, Inc
 
consistency regularization for generative adversarial networks_review
consistency regularization for generative adversarial networks_reviewconsistency regularization for generative adversarial networks_review
consistency regularization for generative adversarial networks_review
Yoonho Na
 
D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)
D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)
D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)
Universitat Politècnica de Catalunya
 
How Do Gain and Discount Functions Affect the Correlation between DCG and Use...
How Do Gain and Discount Functions Affect the Correlation between DCG and Use...How Do Gain and Discount Functions Affect the Correlation between DCG and Use...
How Do Gain and Discount Functions Affect the Correlation between DCG and Use...
Julián Urbano
 
Intro to Deep Reinforcement Learning
Intro to Deep Reinforcement LearningIntro to Deep Reinforcement Learning
Intro to Deep Reinforcement Learning
Khaled Saleh
 
Gradient Boosted Regression Trees in scikit-learn
Gradient Boosted Regression Trees in scikit-learnGradient Boosted Regression Trees in scikit-learn
Gradient Boosted Regression Trees in scikit-learn
DataRobot
 
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
MLconf
 
Computer vision for transportation
Computer vision for transportationComputer vision for transportation
Computer vision for transportation
Wanjin Yu
 
Computer Vision for Beginners
Computer Vision for BeginnersComputer Vision for Beginners
Computer Vision for Beginners
Sanghamitra Deb
 
【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...
【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...
【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...
Deep Learning JP
 
gan.pdf
gan.pdfgan.pdf
gan.pdf
Dr.rukmani Devi
 

Similar to Deep Convolutional GANs - meaning of latent space (20)

Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...
 
IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」
 
Deep Learning in Computer Vision
Deep Learning in Computer VisionDeep Learning in Computer Vision
Deep Learning in Computer Vision
 
# Can we trust ai. the dilemma of model adjustment
# Can we trust ai. the dilemma of model adjustment# Can we trust ai. the dilemma of model adjustment
# Can we trust ai. the dilemma of model adjustment
 
Implementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on SparkImplementation of linear regression and logistic regression on Spark
Implementation of linear regression and logistic regression on Spark
 
Introduction to Genetic algorithm and its significance in VLSI design and aut...
Introduction to Genetic algorithm and its significance in VLSI design and aut...Introduction to Genetic algorithm and its significance in VLSI design and aut...
Introduction to Genetic algorithm and its significance in VLSI design and aut...
 
Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsUsing Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning Models
 
Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsUsing Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning Models
 
MLSEV. Logistic Regression, Deepnets, and Time Series
MLSEV. Logistic Regression, Deepnets, and Time Series MLSEV. Logistic Regression, Deepnets, and Time Series
MLSEV. Logistic Regression, Deepnets, and Time Series
 
BSSML17 - Deepnets
BSSML17 - DeepnetsBSSML17 - Deepnets
BSSML17 - Deepnets
 
consistency regularization for generative adversarial networks_review
consistency regularization for generative adversarial networks_reviewconsistency regularization for generative adversarial networks_review
consistency regularization for generative adversarial networks_review
 
D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)
D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)
D1L5 Visualization (D1L2 Insight@DCU Machine Learning Workshop 2017)
 
How Do Gain and Discount Functions Affect the Correlation between DCG and Use...
How Do Gain and Discount Functions Affect the Correlation between DCG and Use...How Do Gain and Discount Functions Affect the Correlation between DCG and Use...
How Do Gain and Discount Functions Affect the Correlation between DCG and Use...
 
Intro to Deep Reinforcement Learning
Intro to Deep Reinforcement LearningIntro to Deep Reinforcement Learning
Intro to Deep Reinforcement Learning
 
Gradient Boosted Regression Trees in scikit-learn
Gradient Boosted Regression Trees in scikit-learnGradient Boosted Regression Trees in scikit-learn
Gradient Boosted Regression Trees in scikit-learn
 
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
 
Computer vision for transportation
Computer vision for transportationComputer vision for transportation
Computer vision for transportation
 
Computer Vision for Beginners
Computer Vision for BeginnersComputer Vision for Beginners
Computer Vision for Beginners
 
【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...
【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...
【DL輪読会】Toward Fast and Stabilized GAN Training for Highfidelity Few-shot Imag...
 
gan.pdf
gan.pdfgan.pdf
gan.pdf
 

More from Hansol Kang

ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )
ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )
ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )
Hansol Kang
 
관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)
관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)
관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)
Hansol Kang
 
알아두면 쓸모있는 깃허브 2
알아두면 쓸모있는 깃허브 2알아두면 쓸모있는 깃허브 2
알아두면 쓸모있는 깃허브 2
Hansol Kang
 
알아두면 쓸모있는 깃허브 1
알아두면 쓸모있는 깃허브 1알아두면 쓸모있는 깃허브 1
알아두면 쓸모있는 깃허브 1
Hansol Kang
 
FPN 리뷰
FPN 리뷰FPN 리뷰
FPN 리뷰
Hansol Kang
 
R-FCN 리뷰
R-FCN 리뷰R-FCN 리뷰
R-FCN 리뷰
Hansol Kang
 
basic of deep learning
basic of deep learningbasic of deep learning
basic of deep learning
Hansol Kang
 
파이썬 제대로 활용하기
파이썬 제대로 활용하기파이썬 제대로 활용하기
파이썬 제대로 활용하기
Hansol Kang
 
모던 C++ 정리
모던 C++ 정리모던 C++ 정리
모던 C++ 정리
Hansol Kang
 
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...
Hansol Kang
 
문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)
문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)
문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)
Hansol Kang
 
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)
Hansol Kang
 
HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법
HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법
HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법
Hansol Kang
 
QT 프로그래밍 기초(basic of QT programming tutorial)
QT 프로그래밍 기초(basic of QT programming tutorial)QT 프로그래밍 기초(basic of QT programming tutorial)
QT 프로그래밍 기초(basic of QT programming tutorial)
Hansol Kang
 
Continuously Adaptive Mean Shift(CAMSHIFT)
Continuously Adaptive Mean Shift(CAMSHIFT)Continuously Adaptive Mean Shift(CAMSHIFT)
Continuously Adaptive Mean Shift(CAMSHIFT)
Hansol Kang
 
Mobile Robot PD and DOB control
Mobile Robot PD and DOB controlMobile Robot PD and DOB control
Mobile Robot PD and DOB control
Hansol Kang
 
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)
Hansol Kang
 
머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)
머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)
머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)
Hansol Kang
 
서포트 벡터 머신(Support Vector Machine, SVM)
서포트 벡터 머신(Support Vector Machine, SVM)서포트 벡터 머신(Support Vector Machine, SVM)
서포트 벡터 머신(Support Vector Machine, SVM)
Hansol Kang
 

More from Hansol Kang (19)

ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )
ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )
ROS 시작하기(Getting Started with ROS:: Your First Steps in Robot Programming )
 
관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)
관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)
관측 임무스케줄링 (Selecting and scheduling observations of agile satellites)
 
알아두면 쓸모있는 깃허브 2
알아두면 쓸모있는 깃허브 2알아두면 쓸모있는 깃허브 2
알아두면 쓸모있는 깃허브 2
 
알아두면 쓸모있는 깃허브 1
알아두면 쓸모있는 깃허브 1알아두면 쓸모있는 깃허브 1
알아두면 쓸모있는 깃허브 1
 
FPN 리뷰
FPN 리뷰FPN 리뷰
FPN 리뷰
 
R-FCN 리뷰
R-FCN 리뷰R-FCN 리뷰
R-FCN 리뷰
 
basic of deep learning
basic of deep learningbasic of deep learning
basic of deep learning
 
파이썬 제대로 활용하기
파이썬 제대로 활용하기파이썬 제대로 활용하기
파이썬 제대로 활용하기
 
모던 C++ 정리
모던 C++ 정리모던 C++ 정리
모던 C++ 정리
 
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...
 
문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)
문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)
문서와 개발에 필요한 간단한 팁들(Too easy, but important things - document, development)
 
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)
 
HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법
HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법
HSV 컬러 공간에서의 레티넥스와 채도 보정을 이용한 화질 개선 기법
 
QT 프로그래밍 기초(basic of QT programming tutorial)
QT 프로그래밍 기초(basic of QT programming tutorial)QT 프로그래밍 기초(basic of QT programming tutorial)
QT 프로그래밍 기초(basic of QT programming tutorial)
 
Continuously Adaptive Mean Shift(CAMSHIFT)
Continuously Adaptive Mean Shift(CAMSHIFT)Continuously Adaptive Mean Shift(CAMSHIFT)
Continuously Adaptive Mean Shift(CAMSHIFT)
 
Mobile Robot PD and DOB control
Mobile Robot PD and DOB controlMobile Robot PD and DOB control
Mobile Robot PD and DOB control
 
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)
딥러닝 기초 - XOR 문제와 딥뉴럴넷(Basic of DL - XOR problem and DNN)
 
머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)
머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)
머신 러닝 기초 - 회귀 및 분류 (Basic of ML - Regression and Classification)
 
서포트 벡터 머신(Support Vector Machine, SVM)
서포트 벡터 머신(Support Vector Machine, SVM)서포트 벡터 머신(Support Vector Machine, SVM)
서포트 벡터 머신(Support Vector Machine, SVM)
 

Recently uploaded

Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
Uni Systems S.M.S.A.
 
Data structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdfData structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdf
TIPNGVN2
 
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 daysPushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Adtran
 
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
shyamraj55
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
Octavian Nadolu
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Malak Abu Hammad
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Neo4j
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neo4j
 
PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
ControlCase
 
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Zilliz
 
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
James Anderson
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
SOFTTECHHUB
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems S.M.S.A.
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Matthew Sinclair
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
Alpen-Adria-Universität
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Paige Cruz
 
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIEnchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Vladimir Iglovikov, Ph.D.
 
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
KatiaHIMEUR1
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Edge AI and Vision Alliance
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 

Recently uploaded (20)

Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
 
Data structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdfData structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdf
 
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 daysPushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
 
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
 
PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
 
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
 
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
 
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIEnchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
 
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 

Deep Convolutional GANs - meaning of latent space

  • 1. Deep Convolutional GANs ISL Lab Seminar Hansol Kang : Meaning of Latent Space
  • 3. Review of GAN 2018-10-05 3 • Adversarial nets 1) Global Optimality of datag pp  2) Convergence of Algorithm D GVs x )(xpdata “Generative Adversarial Networks” Goal Method
  • 4. D DCGAN 2018-10-05 4 • Introduction * Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015). * “I have the strongest MLP army.” “I have too.” G
  • 5.
  • 6. DCGAN 2018-10-05 6 • Introduction * D G “What are they doing?” “We have a better CNN than MLP” D “I have the strongest MLP army.” “I have too.” G Vanilla GAN DCGAN * Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
  • 7. DCGAN 2018-10-05 7 • Contributions Generating Natural Image Deep Convolutional GANs Image Classification using D Filter Visualization Vector arithmetic properties
  • 8. Z“I’m very Important” Who am I? Black box Real D A B C DCGAN 2018-10-05 8 • Contributions Generating Natural Image Deep Convolutional GANs Image Classification using D Filter Visualization Vector arithmetic properties
  • 9. DCGAN 2018-10-05 9 • Approach and Model Architecture Replace any pooling layers with strided convolutions (discriminator) and fractional- strided convolutions (generator). Use batchnorm in both the generator and the discriminator. Remove fully connected hidden layers for deeper architectures. Use ReLU activation in generator for all layers except for the output, which uses Tanh. Use LeakyReLU activation in the discriminator for all layers.
  • 10. DCGAN 2018-10-05 10 • Approach and Model Architecture Strided Convolution Fractional Convolution(Transposed Convolution)
  • 11. DCGAN 2018-10-05 11 • Approach and Model Architecture Batch Normalization Except for these layers. Output layer of Generator Input layer of Discriminator
  • 12. DCGAN 2018-10-05 12 • Approach and Model Architecture No fully connected layer Classical CNN GAP(Global Average Pooling) http://nmhkahn.github.io/Casestudy-CNN
  • 13. DCGAN 2018-10-05 13 • Approach and Model Architecture No fully connected layer https://raw.githubusercontent.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN/master/pytorch_DCGAN.png
  • 14. DCGAN 2018-10-05 14 • Approach and Model Architecture ReLU, Tanh, LeakyReLU http://gmelli.org/RKB/Rectified_Linear_Unit_(ReLU)_Activation_Function Generator : ReLU, Tanh Discriminator : LeakyReLu , Sigmoid
  • 15. DCGAN 2018-10-05 15 • Details of Adversarial Training • Mini-batch stochastic gradient descent(SGD); mini-batch size of 128 • All weights initialized from a zero-centered Normal distribution with standard deviation 0.02 • Leaky slope 0.2 • Adam optimizer; lr =0.0002, beta1 = 0.9, beta2 = 0.5
  • 16. DCGAN 2018-10-05 16 • Details of Adversarial Training LSUN dataset 1 epoch
  • 17. DCGAN 2018-10-05 17 • Details of Adversarial Training LSUN dataset 5 epochs
  • 18. DCGAN 2018-10-05 18 • Empirical Validation of DCGANs Capabilities • CIFAR-10 • Classification • Domain robustness
  • 19. DCGAN 2018-10-05 19 • Empirical Validation of DCGANs Capabilities SVHN(Street View House Numbers) dataset
  • 20. DCGAN 2018-10-05 20 • Investigating and Visualizing The Internals of The Networks Walking in the latent space
  • 21. DCGAN 2018-10-05 21 • Investigating and Visualizing The Internals of The Networks(cont.) Visualizing the discriminator features
  • 22. DCGAN 2018-10-05 22 • Investigating and Visualizing The Internals of The Networks(cont.) Forgetting to draw certain objects in charge of windows in charge of beds in charge of lamps in charge of doors … Latent code Filters(Conv) Generation 1 0 0 Noise(z)
  • 23. DCGAN 2018-10-05 23 • Investigating and Visualizing The Internals of The Networks(cont.) Forgetting to draw certain objects
  • 24. DCGAN 2018-10-05 24 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
  • 25. DCGAN 2018-10-05 25 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
  • 26. DCGAN 2018-10-05 26 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
  • 27. DCGAN 2018-10-05 27 • Investigating and Visualizing The Internals of The Networks(cont.) Vector arithmetic on face samples
  • 30. Experiment • Results#1 CelebA 2018-10-05 30 Ground Truth Vanilla GAN : DCGAN : Epoch 1 Epoch 5 Epoch 100 Epoch 1 Epoch 5 Epoch 30 Still have this sample Results are cherry picked
  • 31. Experiment • Results#2 LSUN) 2018-10-05 31 Ground Truth Vanilla GAN : DCGAN : Epoch 1 Epoch 5 Epoch 12 Epoch 1 Epoch 2 Epoch 5 Results are cherry picked
  • 32. Experiment • Results#3 Korean Idol – Transfer trial 2018-10-05 32 • I used weights and biases generated by celebA learning. • I wanted the effect of transfer learning but failed. Maybe these factors (Asian, cropping image) Ground Truth Epoch 1 Epoch 2 Epoch 3 Epoch 4 Epoch 5 Epoch 6
  • 33. Experiment • Results#4 Korean Idol 2018-10-05 33 Ground Truth Epoch 1 Epoch 5 Epoch 30 Epoch 50 Epoch 100 Epoch 150 • 10000 images Insufficient data set
  • 34. Summary 2018-10-05 34 • Stable set of architectures for training generative adversarial networks • Good representations of images for supervised learning and generative modeling • Sometimes collapse a subset of filters to a single oscillating mode • Latent code has a special meaning, not a simple noise component. [Instability of GAN]
  • 35. Future work 2018-10-05 35 Paper Review Vanilla GAN DCGAN InfoGAN Unrolled GAN Wasserstein GAN LS GAN BEGAN Pix2Pix Cycle GAN Proposed Model SpyGAN Tips Document Programming Mathematical Study Information theory (working title)
  • 36. &
  • 37. Appendix • Issues at the VAE Seminar (18.07.23) 2018-10-05 37  Issue#1 Performance of VAE and GAN  Issue#2 Log likelihood  Issue#3 Dimension of latent code  Issue#4 Why manifold? Durk Kingma 1. Adam: A Method for Stochastic Optimization 2. Auto-Encoding Variational Bayes Machine Learning researcher at OpenAI - Mathematically very difficult papers Intuitive explanation https://www.youtube.com/watch?v=o_peo6U7IRM 오토인코더의 모든 것 : I refer to this video
  • 38. Appendix • Issue #1 Performance of VAE and GAN 2018-10-05 38 “Compared to GAN, VAE is relatively blurred and I do not know why.” “Cost function”     )(||)|()(|log),,( )|( zpxzqKLzgxExL xzq    ),,(min xL VAE ),(maxmin DGV DG       zGDExDEDGV zdata pzpx  1log)(log),( ~~ GAN Intuition Reconstruction Error Regularization ≈ D Loss ≈ G Loss
  • 39. Appendix • Issue #1 Performance of VAE and GAN 2018-10-05 39 VAE Loss= Recon. Error + Regularization GAN Loss= G_Loss + D_Loss E D Recon. Error D Real Fake 1. Optimize 2. Image Quality 3. Generalization VAE vs. GAN
  • 40. Appendix • Issue #2 Log likelihood 2018-10-05 40 Question about log likelihood “Summation and monotonically increasing” MLE(Maximum Likelihood Estimation) : Unknown parameter estimation from observation )|(maxargˆ   yp eg. Gaussian Distribution Mean and Std  i iyp )|(maxarg          i i i i ypyp )|(logmaxarg)|(logmaxarg   : monotonically increasing function1 Log(x) cf.  i ixp )(logmaxarg   Generation model
  • 41. Appendix • Issue #3 Dimension of latent code 2018-10-05 41 “Is the latent code dimension always small?” “Yes” AE, What’s this? Dimension reduction E D High Low Interested Sparse AE FAILED
  • 42. Appendix • Issue #4 Why manifold? 2018-10-05 42 What’s the manifold and Why explain the manifold? “Concept of manifold and Difference of between AE and VAE” High Low Subspace =Manifold Concept of manifold D Purpose of AE : Manifold Learning Purpose of AE and VAE Assumption(manifold hypothesis) Uniform sampling E Unsupervised Learning D Purpose of VAE : Generative Model E Unsupervised Learning : Correlation between generation and manifold…
  • 43. Appendix • PyTorch (Variable length inputs) 2018-10-05 43 Shape = {Size} torch.Size([128, 3, 32, 32]) Shape = {Size} torch.Size([128, 64, 16, 16]) Shape = {Size} torch.Size([128, 16384])3x32x32 CIFAR-10 Shape = {Size} torch.Size([128, 64, 109, 89]) Shape = {Size} torch.Size([128, 3, 218, 178]) Shape = {Size} torch.Size([128, 620864]) 3x178x218 CelebA Conv Input Pool FC Conv2d(in_ch, out_ch, k_size, s, p) Reshape(bat_sz,-1) Input size is not fixed.