SlideShare a Scribd company logo
1 of 40
Download to read offline
Generative Adversarial Network
NamHyuk Ahn
Generative Adversarial Network
What is a Generative model?
• Goal: Wish to learn X → Y, P(y|x)
• Discriminative model (classifier)
• Directly learn conditional distribution P(y|x) from training data
• SVM, Logistic Regression
• Generative model (classifier)
• Learn the joint probability, P(x,y) = P(x|y) * P(y)
• Estimate parameter of P(x|y), P(y) from training data
• Use Bayes rule to calculate P(y|x)
• Naive Bayes, GMM
Generative vs. Discriminative
• Generative
• Probabilistic “model” of each class
• Decision boundary is “where model becomes more likely”
• Natural use of unlabeled data (unsupervised learning)
• Discriminative
• Focus on the decision boundary
• More powerful with lots of data
• Only supervised task
IAML2.23: Generative vs. discriminative learning
Generative vs. Discriminative
http://slideplayer.com/slide/6982498/
Adversarial Learning
http://www.slideshare.net/xavigiro/deep-learning-for-computer-
vision-generative-models-and-adversarial-training-upc-2016
Adversarial Learning
http://www.slideshare.net/xavigiro/deep-learning-for-computer-
vision-generative-models-and-adversarial-training-upc-2016
counterfeiters
police
fake currency
real currency
(neural) Network
http://cs231n.github.io
Adversarial Network
• Notations
• 𝑝": Generator’s distribution over data 𝑥
• 𝑝$(𝒛): Prior on input noise
• 𝐺(𝑧; 𝜃"): Generator function (mlp with parameters 𝜃")
• 𝐷(𝑥; 𝜃-): Discriminator function output single scalar
• If input is from real distribution, output is 1, otherwise return 0
• Goals (cost function)
Training procedure
• Optimize D completely in inner loop is bad idea
(computation prohibitive, overfitting)
• Instead, first optimize D k steps and optimize G one step
• D being maintained near optimal solution
• G slowly moves to optimal
• In practice, eq1 may not provide sufficient gradient for G
• In early stage, G output poor example, so D can reject with high
confidence
• log	(1 − 𝐷 𝐺 𝒛 ) saturate (log 1 = 0)
• Rather training G to minimize log	(1 − 𝐷 𝐺 𝒛 ),
maximize log	𝐷(𝐺 𝒛 )
Global Optimality of 𝑝" = 𝑝-676
Global Optimality of 𝑝" = 𝑝-676
Convergence of Algorithm 1
Theoretically, cool.
But in practice, GAN not always show good performance
Result
Why GAN is important?
• Use GAN in semi-supervised Learning
• Features from discriminator could improve performance
when limited labeled data is available
• Vector Arithmetic
• Generate fake image of bedroom (DCGAN)
• [man with glasses] - [man without glasses] + [woman without
glasses]
= [woman with glasses]
• Conditional GAN
• GAN performs unsupervised manner, but also can model 𝑝 𝑥 𝑐
by adding class label in both G and D
Deep Generative Image Models using a
Laplacian Pyramid of Adversarial Networks
Image Pyramid
• Image pyramid is multi-scale
image representation
• Generate pyramid
• Blur previous pyramid image
• Subsample pixels
• Variety type of pyramid
• Gaussian pyramid
• Laplacian pyramid
• ...
Figure from David Forsyth
Laplacian Pyramid
http://cs.brown.edu/courses/csci1430/2011/results/proj1/georg
em/explained.jpg
Conditional GAN
• Add conditional variable
→ 𝐷 𝑥 𝑐 	𝑜𝑟	𝐺 𝑧 𝑐
• Variable 𝑐 can be anything
• Class label
• Tags correspond to image
• Additional image information
• ...
Laplacian Pyramid GAN : Sampling procedure
Laplacian Pyramid GAN : Sampling procedure
Start with generator that output
scaled image (Gaussian pyramid) 𝐼<=
Laplacian Pyramid GAN : Sampling procedure
1. Upsample Gaussian pyramid 𝐼<= to 𝑙? (green arrow)
2. Input noise 𝑧?	and 𝑙? to generator
(𝑙? is conditional information – orange arrow)
3. Generator output Laplacian pyramid ℎA?
Laplacian Pyramid GAN : Sampling procedure
1. Upsample Gaussian pyramid 𝐼<? to 𝑙B (green arrow)
2. Input noise 𝑧B	and 𝑙B to generator
(𝑙B is conditional information – orange arrow)
3. Generator output Laplacian pyramid ℎAB
Laplacian Pyramid GAN : Sampling procedure
Finally create generated image 𝐼<C
Laplacian Pyramid GAN : Training procedure
Result
Result
Believe or not
Why LAPGAN is better?
• LAPGAN don’t use global Generator/Discriminator
• Instead, separate image into multi-scaled pyramids
• Other multi-scaled approach might be helpful
• Each G/D only cover each scaled pyramid
• Believe or not, LAPGAN produce sharper images
• (my thought)
• Each Generator focus on generating Laplacian pyramid
which about high-pass (edges) with conditional information
• This idea can make generator to produce much sharper images
Other GAN topics
• GAN
• LAPGAN
• DCGAN
• InfoGAN
• Bidirectional GAN
• EBGAN
• ...
• ...
DCGAN (15.11)
EBGAN (16.09)
StackGAN (16.12)
StackGAN (16.12)
Reference
• Goodfellow, Ian, et al. "Generative adversarial nets." Advances in Neural
Information Processing Systems. 2014.
• Denton, Emily L., Soumith Chintala, and Rob Fergus. "Deep Generative
Image Models using a Laplacian Pyramid of Adversarial Networks."
Advances in neural information processing systems. 2015.

More Related Content

What's hot

GAN - Theory and Applications
GAN - Theory and ApplicationsGAN - Theory and Applications
GAN - Theory and ApplicationsEmanuele Ghelfi
 
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Manohar Mukku
 
Deep neural networks
Deep neural networksDeep neural networks
Deep neural networksSi Haem
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksYunjey Choi
 
Super resolution in deep learning era - Jaejun Yoo
Super resolution in deep learning era - Jaejun YooSuper resolution in deep learning era - Jaejun Yoo
Super resolution in deep learning era - Jaejun YooJaeJun Yoo
 
Convolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsConvolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsKasun Chinthaka Piyarathna
 
Optic flow estimation with deep learning
Optic flow estimation with deep learningOptic flow estimation with deep learning
Optic flow estimation with deep learningYu Huang
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial NetworksDong Heon Cho
 
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기1시간만에 GAN(Generative Adversarial Network) 완전 정복하기
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기NAVER Engineering
 
Object Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning FrameworkObject Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning FrameworkNader Karimi
 
Stable Diffusion path
Stable Diffusion pathStable Diffusion path
Stable Diffusion pathVitaly Bondar
 
Optimization in Deep Learning
Optimization in Deep LearningOptimization in Deep Learning
Optimization in Deep LearningYan Xu
 
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)Fellowship at Vodafone FutureLab
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMustafa Yagmur
 
Reinforcement Learning
Reinforcement LearningReinforcement Learning
Reinforcement Learningbutest
 
You Only Look Once: Unified, Real-Time Object Detection
You Only Look Once: Unified, Real-Time Object DetectionYou Only Look Once: Unified, Real-Time Object Detection
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
 
Generative Adversarial Networks and Their Medical Imaging Applications
Generative Adversarial Networks and Their Medical Imaging ApplicationsGenerative Adversarial Networks and Their Medical Imaging Applications
Generative Adversarial Networks and Their Medical Imaging ApplicationsKyuhwan Jung
 

What's hot (20)

GAN - Theory and Applications
GAN - Theory and ApplicationsGAN - Theory and Applications
GAN - Theory and Applications
 
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)
 
Deep neural networks
Deep neural networksDeep neural networks
Deep neural networks
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
Super resolution in deep learning era - Jaejun Yoo
Super resolution in deep learning era - Jaejun YooSuper resolution in deep learning era - Jaejun Yoo
Super resolution in deep learning era - Jaejun Yoo
 
Convolutional Neural Network and Its Applications
Convolutional Neural Network and Its ApplicationsConvolutional Neural Network and Its Applications
Convolutional Neural Network and Its Applications
 
Image segmentation using wvlt trnsfrmtn and fuzzy logic. ppt
Image segmentation using wvlt trnsfrmtn and fuzzy logic. pptImage segmentation using wvlt trnsfrmtn and fuzzy logic. ppt
Image segmentation using wvlt trnsfrmtn and fuzzy logic. ppt
 
Optic flow estimation with deep learning
Optic flow estimation with deep learningOptic flow estimation with deep learning
Optic flow estimation with deep learning
 
Wasserstein GAN
Wasserstein GANWasserstein GAN
Wasserstein GAN
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial Networks
 
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기1시간만에 GAN(Generative Adversarial Network) 완전 정복하기
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기
 
Object Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning FrameworkObject Detection Using R-CNN Deep Learning Framework
Object Detection Using R-CNN Deep Learning Framework
 
Stable Diffusion path
Stable Diffusion pathStable Diffusion path
Stable Diffusion path
 
Optimization in Deep Learning
Optimization in Deep LearningOptimization in Deep Learning
Optimization in Deep Learning
 
U-Net (1).pptx
U-Net (1).pptxU-Net (1).pptx
U-Net (1).pptx
 
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
AlexNet(ImageNet Classification with Deep Convolutional Neural Networks)
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial Networks
 
Reinforcement Learning
Reinforcement LearningReinforcement Learning
Reinforcement Learning
 
You Only Look Once: Unified, Real-Time Object Detection
You Only Look Once: Unified, Real-Time Object DetectionYou Only Look Once: Unified, Real-Time Object Detection
You Only Look Once: Unified, Real-Time Object Detection
 
Generative Adversarial Networks and Their Medical Imaging Applications
Generative Adversarial Networks and Their Medical Imaging ApplicationsGenerative Adversarial Networks and Their Medical Imaging Applications
Generative Adversarial Networks and Their Medical Imaging Applications
 

Viewers also liked

TensorFlow Tutorial
TensorFlow TutorialTensorFlow Tutorial
TensorFlow TutorialNamHyuk Ahn
 
Generative adversarial nets
Generative adversarial netsGenerative adversarial nets
Generative adversarial netsKeisuke Hosaka
 
Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...
Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...
Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...Universitat Politècnica de Catalunya
 
Lecture10
Lecture10Lecture10
Lecture10zukun
 
Sparsity Based Super Resolution Using Color Channel Constraints
Sparsity Based Super Resolution Using Color Channel ConstraintsSparsity Based Super Resolution Using Color Channel Constraints
Sparsity Based Super Resolution Using Color Channel ConstraintsHojjat Seyed Mousavi
 
Deep learning for image super resolution
Deep learning for image super resolutionDeep learning for image super resolution
Deep learning for image super resolutionPrudhvi Raj
 
Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsArtifacia
 
Super Resolution in Digital Image processing
Super Resolution in Digital Image processingSuper Resolution in Digital Image processing
Super Resolution in Digital Image processingRamrao Desai
 
Deep Advances in Generative Modeling
Deep Advances in Generative ModelingDeep Advances in Generative Modeling
Deep Advances in Generative Modelingindico data
 
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIGenerative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
 
20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영Tae Young Lee
 
Slideshare signup tutorial
Slideshare signup tutorialSlideshare signup tutorial
Slideshare signup tutorialbestabrook
 
Feature Extraction
Feature ExtractionFeature Extraction
Feature Extractionskylian
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMark Chang
 
2017 tensor flow dev summit
2017 tensor flow dev summit2017 tensor flow dev summit
2017 tensor flow dev summitTae Young Lee
 
論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural Networks論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural NetworksSeiya Tokui
 
텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016
텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016
텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016Taehoon Kim
 

Viewers also liked (18)

TensorFlow Tutorial
TensorFlow TutorialTensorFlow Tutorial
TensorFlow Tutorial
 
Generative adversarial nets
Generative adversarial netsGenerative adversarial nets
Generative adversarial nets
 
Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...
Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...
Generative Adversarial Networks (D2L5 Deep Learning for Speech and Language U...
 
Lecture10
Lecture10Lecture10
Lecture10
 
Sparsity Based Super Resolution Using Color Channel Constraints
Sparsity Based Super Resolution Using Color Channel ConstraintsSparsity Based Super Resolution Using Color Channel Constraints
Sparsity Based Super Resolution Using Color Channel Constraints
 
Pixel Recursive Super Resolution. Google Brain
 Pixel Recursive Super Resolution.  Google Brain Pixel Recursive Super Resolution.  Google Brain
Pixel Recursive Super Resolution. Google Brain
 
Deep learning for image super resolution
Deep learning for image super resolutionDeep learning for image super resolution
Deep learning for image super resolution
 
Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their Applications
 
Super Resolution in Digital Image processing
Super Resolution in Digital Image processingSuper Resolution in Digital Image processing
Super Resolution in Digital Image processing
 
Deep Advances in Generative Modeling
Deep Advances in Generative ModelingDeep Advances in Generative Modeling
Deep Advances in Generative Modeling
 
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIGenerative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
 
20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영
 
Slideshare signup tutorial
Slideshare signup tutorialSlideshare signup tutorial
Slideshare signup tutorial
 
Feature Extraction
Feature ExtractionFeature Extraction
Feature Extraction
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial Networks
 
2017 tensor flow dev summit
2017 tensor flow dev summit2017 tensor flow dev summit
2017 tensor flow dev summit
 
論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural Networks論文紹介 Pixel Recurrent Neural Networks
論文紹介 Pixel Recurrent Neural Networks
 
텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016
텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016
텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016
 

Similar to Generative Adversarial Network (+Laplacian Pyramid GAN)

Reading group gan - 20170417
Reading group   gan - 20170417Reading group   gan - 20170417
Reading group gan - 20170417Shuai Zhang
 
Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”
Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”
Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”Lviv Startup Club
 
Tutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksTutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksMLReview
 
GANs Deep Learning Summer School
GANs Deep Learning Summer SchoolGANs Deep Learning Summer School
GANs Deep Learning Summer SchoolRubens Zimbres, PhD
 
Machine Learning workshop by GDSC Amity University Chhattisgarh
Machine Learning workshop by GDSC Amity University ChhattisgarhMachine Learning workshop by GDSC Amity University Chhattisgarh
Machine Learning workshop by GDSC Amity University ChhattisgarhPoorabpatel
 
# 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 adjustmentTerence Huang
 
Demystifying deep reinforement learning
Demystifying deep reinforement learningDemystifying deep reinforement learning
Demystifying deep reinforement learning재연 윤
 
ngboost.pptx
ngboost.pptxngboost.pptx
ngboost.pptxHadrian7
 
DeepLearningLecture.pptx
DeepLearningLecture.pptxDeepLearningLecture.pptx
DeepLearningLecture.pptxssuserf07225
 
Deep Convolutional GANs - meaning of latent space
Deep Convolutional GANs - meaning of latent spaceDeep Convolutional GANs - meaning of latent space
Deep Convolutional GANs - meaning of latent spaceHansol Kang
 
KDGAN: Knowledge Distillation with Generative Adversarial Networks
KDGAN: Knowledge Distillation with Generative Adversarial NetworksKDGAN: Knowledge Distillation with Generative Adversarial Networks
KDGAN: Knowledge Distillation with Generative Adversarial NetworksSungchul Kim
 
Hadoop Summit 2010 Machine Learning Using Hadoop
Hadoop Summit 2010 Machine Learning Using HadoopHadoop Summit 2010 Machine Learning Using Hadoop
Hadoop Summit 2010 Machine Learning Using HadoopYahoo Developer Network
 
Image anomaly detection with generative adversarial networks
Image anomaly detection with generative adversarial networksImage anomaly detection with generative adversarial networks
Image anomaly detection with generative adversarial networksSakshiSingh480
 
Deep Generative Models
Deep Generative ModelsDeep Generative Models
Deep Generative ModelsMijung Kim
 

Similar to Generative Adversarial Network (+Laplacian Pyramid GAN) (20)

gan.pdf
gan.pdfgan.pdf
gan.pdf
 
Reading group gan - 20170417
Reading group   gan - 20170417Reading group   gan - 20170417
Reading group gan - 20170417
 
Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”
Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”
Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”
 
Tutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksTutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial Networks
 
GANs Deep Learning Summer School
GANs Deep Learning Summer SchoolGANs Deep Learning Summer School
GANs Deep Learning Summer School
 
Semi-Supervised Learning with GANs by Olga Petrova, Machine Learning Engineer...
Semi-Supervised Learning with GANs by Olga Petrova, Machine Learning Engineer...Semi-Supervised Learning with GANs by Olga Petrova, Machine Learning Engineer...
Semi-Supervised Learning with GANs by Olga Petrova, Machine Learning Engineer...
 
Machine Learning workshop by GDSC Amity University Chhattisgarh
Machine Learning workshop by GDSC Amity University ChhattisgarhMachine Learning workshop by GDSC Amity University Chhattisgarh
Machine Learning workshop by GDSC Amity University Chhattisgarh
 
# 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
 
Demystifying deep reinforement learning
Demystifying deep reinforement learningDemystifying deep reinforement learning
Demystifying deep reinforement learning
 
ngboost.pptx
ngboost.pptxngboost.pptx
ngboost.pptx
 
DeepLearningLecture.pptx
DeepLearningLecture.pptxDeepLearningLecture.pptx
DeepLearningLecture.pptx
 
Deep Convolutional GANs - meaning of latent space
Deep Convolutional GANs - meaning of latent spaceDeep Convolutional GANs - meaning of latent space
Deep Convolutional GANs - meaning of latent space
 
ngboost.pptx
ngboost.pptxngboost.pptx
ngboost.pptx
 
InfoGAIL
InfoGAIL InfoGAIL
InfoGAIL
 
KDGAN: Knowledge Distillation with Generative Adversarial Networks
KDGAN: Knowledge Distillation with Generative Adversarial NetworksKDGAN: Knowledge Distillation with Generative Adversarial Networks
KDGAN: Knowledge Distillation with Generative Adversarial Networks
 
Hadoop Summit 2010 Machine Learning Using Hadoop
Hadoop Summit 2010 Machine Learning Using HadoopHadoop Summit 2010 Machine Learning Using Hadoop
Hadoop Summit 2010 Machine Learning Using Hadoop
 
11_gan.pdf
11_gan.pdf11_gan.pdf
11_gan.pdf
 
K-means and GMM
K-means and GMMK-means and GMM
K-means and GMM
 
Image anomaly detection with generative adversarial networks
Image anomaly detection with generative adversarial networksImage anomaly detection with generative adversarial networks
Image anomaly detection with generative adversarial networks
 
Deep Generative Models
Deep Generative ModelsDeep Generative Models
Deep Generative Models
 

More from NamHyuk Ahn

Supporting Time-Sensitive Applications on a Commodity OS
Supporting Time-Sensitive Applications on a Commodity OSSupporting Time-Sensitive Applications on a Commodity OS
Supporting Time-Sensitive Applications on a Commodity OSNamHyuk Ahn
 
Single Shot Multibox Detector
Single Shot Multibox DetectorSingle Shot Multibox Detector
Single Shot Multibox DetectorNamHyuk Ahn
 
Multimodal Residual Learning for Visual QA
Multimodal Residual Learning for Visual QAMultimodal Residual Learning for Visual QA
Multimodal Residual Learning for Visual QANamHyuk Ahn
 
Google's Multilingual Neural Machine Translation System
Google's Multilingual Neural Machine Translation SystemGoogle's Multilingual Neural Machine Translation System
Google's Multilingual Neural Machine Translation SystemNamHyuk Ahn
 
DeconvNet, DecoupledNet, TransferNet in Image Segmentation
DeconvNet, DecoupledNet, TransferNet in Image SegmentationDeconvNet, DecoupledNet, TransferNet in Image Segmentation
DeconvNet, DecoupledNet, TransferNet in Image SegmentationNamHyuk Ahn
 
Case Study of Convolutional Neural Network
Case Study of Convolutional Neural NetworkCase Study of Convolutional Neural Network
Case Study of Convolutional Neural NetworkNamHyuk Ahn
 

More from NamHyuk Ahn (6)

Supporting Time-Sensitive Applications on a Commodity OS
Supporting Time-Sensitive Applications on a Commodity OSSupporting Time-Sensitive Applications on a Commodity OS
Supporting Time-Sensitive Applications on a Commodity OS
 
Single Shot Multibox Detector
Single Shot Multibox DetectorSingle Shot Multibox Detector
Single Shot Multibox Detector
 
Multimodal Residual Learning for Visual QA
Multimodal Residual Learning for Visual QAMultimodal Residual Learning for Visual QA
Multimodal Residual Learning for Visual QA
 
Google's Multilingual Neural Machine Translation System
Google's Multilingual Neural Machine Translation SystemGoogle's Multilingual Neural Machine Translation System
Google's Multilingual Neural Machine Translation System
 
DeconvNet, DecoupledNet, TransferNet in Image Segmentation
DeconvNet, DecoupledNet, TransferNet in Image SegmentationDeconvNet, DecoupledNet, TransferNet in Image Segmentation
DeconvNet, DecoupledNet, TransferNet in Image Segmentation
 
Case Study of Convolutional Neural Network
Case Study of Convolutional Neural NetworkCase Study of Convolutional Neural Network
Case Study of Convolutional Neural Network
 

Recently uploaded

11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdfHafizMudaserAhmad
 
KCD Costa Rica 2024 - Nephio para parvulitos
KCD Costa Rica 2024 - Nephio para parvulitosKCD Costa Rica 2024 - Nephio para parvulitos
KCD Costa Rica 2024 - Nephio para parvulitosVictor Morales
 
Artificial Intelligence in Power System overview
Artificial Intelligence in Power System overviewArtificial Intelligence in Power System overview
Artificial Intelligence in Power System overviewsandhya757531
 
Robotics Group 10 (Control Schemes) cse.pdf
Robotics Group 10  (Control Schemes) cse.pdfRobotics Group 10  (Control Schemes) cse.pdf
Robotics Group 10 (Control Schemes) cse.pdfsahilsajad201
 
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...Erbil Polytechnic University
 
CS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdfCS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdfBalamuruganV28
 
DEVICE DRIVERS AND INTERRUPTS SERVICE MECHANISM.pdf
DEVICE DRIVERS AND INTERRUPTS  SERVICE MECHANISM.pdfDEVICE DRIVERS AND INTERRUPTS  SERVICE MECHANISM.pdf
DEVICE DRIVERS AND INTERRUPTS SERVICE MECHANISM.pdfAkritiPradhan2
 
Immutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdfImmutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdfDrew Moseley
 
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.elesangwon
 
Turn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptxTurn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptxStephen Sitton
 
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionUS Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionMebane Rash
 
Paper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdf
Paper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdfPaper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdf
Paper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdfNainaShrivastava14
 
Computer Graphics Introduction, Open GL, Line and Circle drawing algorithm
Computer Graphics Introduction, Open GL, Line and Circle drawing algorithmComputer Graphics Introduction, Open GL, Line and Circle drawing algorithm
Computer Graphics Introduction, Open GL, Line and Circle drawing algorithmDeepika Walanjkar
 
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMSHigh Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMSsandhya757531
 
CME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTES
CME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTESCME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTES
CME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTESkarthi keyan
 
"Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ..."Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ...Erbil Polytechnic University
 
OOP concepts -in-Python programming language
OOP concepts -in-Python programming languageOOP concepts -in-Python programming language
OOP concepts -in-Python programming languageSmritiSharma901052
 
signals in triangulation .. ...Surveying
signals in triangulation .. ...Surveyingsignals in triangulation .. ...Surveying
signals in triangulation .. ...Surveyingsapna80328
 
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor CatchersTechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catcherssdickerson1
 
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSneha Padhiar
 

Recently uploaded (20)

11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf
 
KCD Costa Rica 2024 - Nephio para parvulitos
KCD Costa Rica 2024 - Nephio para parvulitosKCD Costa Rica 2024 - Nephio para parvulitos
KCD Costa Rica 2024 - Nephio para parvulitos
 
Artificial Intelligence in Power System overview
Artificial Intelligence in Power System overviewArtificial Intelligence in Power System overview
Artificial Intelligence in Power System overview
 
Robotics Group 10 (Control Schemes) cse.pdf
Robotics Group 10  (Control Schemes) cse.pdfRobotics Group 10  (Control Schemes) cse.pdf
Robotics Group 10 (Control Schemes) cse.pdf
 
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
 
CS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdfCS 3251 Programming in c all unit notes pdf
CS 3251 Programming in c all unit notes pdf
 
DEVICE DRIVERS AND INTERRUPTS SERVICE MECHANISM.pdf
DEVICE DRIVERS AND INTERRUPTS  SERVICE MECHANISM.pdfDEVICE DRIVERS AND INTERRUPTS  SERVICE MECHANISM.pdf
DEVICE DRIVERS AND INTERRUPTS SERVICE MECHANISM.pdf
 
Immutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdfImmutable Image-Based Operating Systems - EW2024.pdf
Immutable Image-Based Operating Systems - EW2024.pdf
 
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
2022 AWS DNA Hackathon 장애 대응 솔루션 jarvis.
 
Turn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptxTurn leadership mistakes into a better future.pptx
Turn leadership mistakes into a better future.pptx
 
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of ActionUS Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of Action
 
Paper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdf
Paper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdfPaper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdf
Paper Tube : Shigeru Ban projects and Case Study of Cardboard Cathedral .pdf
 
Computer Graphics Introduction, Open GL, Line and Circle drawing algorithm
Computer Graphics Introduction, Open GL, Line and Circle drawing algorithmComputer Graphics Introduction, Open GL, Line and Circle drawing algorithm
Computer Graphics Introduction, Open GL, Line and Circle drawing algorithm
 
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMSHigh Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
High Voltage Engineering- OVER VOLTAGES IN ELECTRICAL POWER SYSTEMS
 
CME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTES
CME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTESCME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTES
CME 397 - SURFACE ENGINEERING - UNIT 1 FULL NOTES
 
"Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ..."Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ...
 
OOP concepts -in-Python programming language
OOP concepts -in-Python programming languageOOP concepts -in-Python programming language
OOP concepts -in-Python programming language
 
signals in triangulation .. ...Surveying
signals in triangulation .. ...Surveyingsignals in triangulation .. ...Surveying
signals in triangulation .. ...Surveying
 
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor CatchersTechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
 
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATIONSOFTWARE ESTIMATION COCOMO AND FP CALCULATION
SOFTWARE ESTIMATION COCOMO AND FP CALCULATION
 

Generative Adversarial Network (+Laplacian Pyramid GAN)