Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
« Le « Machine Learning » – « Apprentissage statistique » ou « Analyse prédictive » - sort des labos de recherche et des cercles de spécialistes pour être de plus en plus être utilisé au sein des entreprises, et pas seulement les startups. En témoigne l’essor de la toolkit OpenSource Scikit-learn très vite répandue internationalement comme l’un des nouveaux standards de cette nouvelle façon de faire du logiciel, mais aussi la disponibilité depuis juillet 2014 d’Azure ML, le service de Machine Learning de Microsoft Azure. Dans cette session nous vous proposons un aperçu du développement de logiciel d’apprentissage statistique en Python avec SciKit-Learn. Nous invitons l'un des principaux contributeurs de cette toolkit, Olivier Grisel , ingénieur de recherche dans l’équipe équipe Inria PARIETAL à Saclay, à venir nous en présenter un aperçu dans une session interactive et basée sur de nombreux exemples et démos. Pour en savoir plus: http://scikit-learn.org https://team.inria.fr/parietal/ https://twitter.com/ogrisel
Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
« Le « Machine Learning » – « Apprentissage statistique » ou « Analyse prédictive » - sort des labos de recherche et des cercles de spécialistes pour être de plus en plus être utilisé au sein des entreprises, et pas seulement les startups. En témoigne l’essor de la toolkit OpenSource Scikit-learn très vite répandue internationalement comme l’un des nouveaux standards de cette nouvelle façon de faire du logiciel, mais aussi la disponibilité depuis juillet 2014 d’Azure ML, le service de Machine Learning de Microsoft Azure. Dans cette session nous vous proposons un aperçu du développement de logiciel d’apprentissage statistique en Python avec SciKit-Learn. Nous invitons l'un des principaux contributeurs de cette toolkit, Olivier Grisel , ingénieur de recherche dans l’équipe équipe Inria PARIETAL à Saclay, à venir nous en présenter un aperçu dans une session interactive et basée sur de nombreux exemples et démos. Pour en savoir plus: http://scikit-learn.org https://team.inria.fr/parietal/ https://twitter.com/ogrisel
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
A brief introduction to deep learning, providing rough interpretation to deep neural networks and simple implementations with Keras for deep learning beginners.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
Slides from my talk at Facebook Developer Circle: Pune Launch on Aug 19 2017. Supplementary code available here: https://github.com/mayurbhangale/pytorch_notebook
PyTorch is one of the most widely used deep learning library in python community. In this talk I will cover the basic to advanced guide to implement deep learning model using PyTorch. My goal is to introduce PyTorch and show how to use it for deep learning project.
Introduction to Machine Learning with Python and scikit-learnMatt Hagy
PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
Presentation on Neural Networks in Tensorflow. Code available at https://github.com/nfmcclure/tensorflow_cookbook . Presentation for Open Source Bridge, Portland, 2016.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
This presentation focuses on Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, with a short introduction to D3, and followed by a TypeScript-based code sample that replicates the TensorFlow playground. Basic knowledge of matrices is helpful.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
NIT Silchar ML Hackathon 2019 Session on Computer Vision with Deep Learning.
Targeted Audience: Pre-requisite: Basic knowledge on Machine Learning and Deep Learning
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
A brief introduction to deep learning, providing rough interpretation to deep neural networks and simple implementations with Keras for deep learning beginners.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
Slides from my talk at Facebook Developer Circle: Pune Launch on Aug 19 2017. Supplementary code available here: https://github.com/mayurbhangale/pytorch_notebook
PyTorch is one of the most widely used deep learning library in python community. In this talk I will cover the basic to advanced guide to implement deep learning model using PyTorch. My goal is to introduce PyTorch and show how to use it for deep learning project.
Introduction to Machine Learning with Python and scikit-learnMatt Hagy
PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
Presentation on Neural Networks in Tensorflow. Code available at https://github.com/nfmcclure/tensorflow_cookbook . Presentation for Open Source Bridge, Portland, 2016.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
This presentation focuses on Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, with a short introduction to D3, and followed by a TypeScript-based code sample that replicates the TensorFlow playground. Basic knowledge of matrices is helpful.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
NIT Silchar ML Hackathon 2019 Session on Computer Vision with Deep Learning.
Targeted Audience: Pre-requisite: Basic knowledge on Machine Learning and Deep Learning
Computer Vision Landscape : Present and FutureSanghamitra Deb
Millions of people all around the world Learn with Chegg. Education at Chegg is powered by the depth and diversity of the content that we have. A huge part of our content is in form of images. These images could be uploaded by students or by content creators. Images contain text that is extracted using a transcription service. Very often uploaded images are noisy. This leads to irrelevant characters or words in the transcribed text. Using object detection techniques we develop a service that extracts the relevant parts of the image and uses a transcription service to get clean text. In the first part of the presentation, I will talk about building an object detection model using YOLO for cropping and masking images to obtain a cleaner text from transcription. YOLO is a deep learning object detection and recognition modeling framework that is able to produce highly accurate results with low latency. In the next part of my presentation, I will talk about the building the Computer Vision landscape at Chegg. Starting from images on academic materials that are composed of elements such as text, equations, diagrams we create a pipeline for extracting these image elements. Using state of the art deep learning techniques we create embeddings for these elements to enhance downstream machine learning models such as content quality and similarity.
Alberto Massidda - Scenes from a memory - Codemotion Rome 2019Codemotion
Generating representations is the ultimate act of creativity. Recent advancements in neural networks (and in processing power) brought us the capability to perform regression against complex samples like images and audio. In this presentation we show the underlying mechanics of media generation from latent space representation of abstract visual ideas, real embodiment of “Platonic” concepts, with Variational Autoencoders, Generative Adversarial Networks, neural style transfer and PixelRNN/CNN along with current practical applications like DeepFake.
Convolutional Neural Networks for Image Classification (Cape Town Deep Learni...Alex Conway
Slides for my talk on:
"Convolutional Neural Networks for Image Classification"
...at the Cape Town Deep Learning Meet-up 20170620
https://www.meetup.com/Cape-Town-deep-learning/events/240485642/
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/08/understanding-dnn-based-object-detectors-a-presentation-from-au-zone-technologies/
Azhar Quddus, Senior Computer Vision Engineer at Au-Zone Technologies, presents the “Understanding DNN-Based Object Detectors” tutorial at the May 2022 Embedded Vision Summit.
Unlike image classifiers, which merely report on the most important objects within or attributes of an image, object detectors determine where objects of interest are located within an image. Consequently, object detectors are central to many computer vision applications including (but not limited to) autonomous vehicles and virtual reality.
In this presentation, Quddus provides a technical introduction to deep-neural-network-based object detectors. He explains how these algorithms work, and how they have evolved in recent years, utilizing examples of popular object detectors. Quddus examines some of the trade-offs to consider when selecting an object detector for an application, and touches on accuracy measurement. He also discusses performance comparison among the models discussed in this presentation.
This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
4. Outline
• Autoencoder
• Autoencoder and variational autoencoder
• VQVAE (VQVAE2)
• Autoregressive model
• PixelCNN
• GAN
• CycleGAN, RecycleGAN
• CycleGAN with guess discriminator
• Flow-based Model
• GLOW
5. Autoencoder
未知的作者 的 此相片 已透過 CC BY-NC 授權 未知的作者 的 此相片 已透過 CC BY-NC 授權
Latent
variables
The latent variables
The variables that can express the
output of the phenomena
1. Denoise
2. Dimension reduction
3. Feature extraction
4. Segmentation
5. Energy measuring
…
7. Variational Autoencoder
未知的作者 的 此相片 已透過 CC BY-NC 授權 未知的作者 的 此相片 已透過 CC BY-NC 授權
mean
variance
The “mean” and
“Variance” could
create a
distribution.
samplingIf we control the latent variables underling the normal
distribution, maybe we might create new latent
variables by sampling from the normal distribution.
The mean and variance should close
to 0 and 1 as best as possible.
策略
1. Latent variables 要呈現
常態分佈。
2. 一些離散的點就給予亂
數做perturbation,期
望把沒有樣本的空間也
填滿。 (其實可以視為
從常態分佈中抽樣)
12. The improvement of VQ
https://arxiv.org/pdf/1803.03382.pdf
The issues from VQ :
1. Only the code selected by encoder will be updated
2. Not all the codes in the codebook will be used
The exponential moving average (EMA) method
Codes
embedding
1. Calculating the distances of the codes that are selected
=> This help to maintain the codebook Selected
code
Codebook 查詢結果
樣本數量
Code的更新Lambda=.999
2. Decomposing the vector into small pieces would help to use them more
efficient.
=> Vector slicing help to use the code more efficiently
找出所有跟該code
相關的embedding,
然後算出這些
embedding的質心,
同時讓這code往這
個質心移動
質心
15. Can we use CNN to handle the autoregressive
model?
• The problem autoregressive model met is …
• “the model cannot get the information from future, but it can use any
information of the past”.
So …
Maybe we can just make a toy sample that:
the model only use the previous information.
(but it would not be the real case in the word for image generating)
16. PixelCNN
1 2 3
4 5 6
7 8 9
kernel
1 2 3
4 5 6
7 8 9
Masked kernel
mask
The pixel CNN which use masked
kernel is proposed with PixelRNN
with LSTM
However, PixelRNN perform better
than PixelCNN.
Some ones thought this would be
caused from LSTM having “gates”
which provide the RNN to handle
complex problems.
17. Blind spots of pixel CNN
• https://towardsdatascience.com/blind-spot-problem-in-pixelcnn-8c71592a14a
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22 23 24 25
The blind spot means: the
generated point is
generated without
considering the spots.
Feature map
1 2 3
4 5 6
7 8 9
kernel
1 2 3
4 5 6
7 8 9
Masked kernel
mask
When running with the masked kernels
1
1 2 3
4 a
If the feature map is handled with zero
padding, then a would be only influenced
with bias terms of NN. (or maybe we can
consider the non-zero padding)
next
1
1 2 3
4a b
a b c d e
f g h i j
n o1k 2l 3m
4p q
Blind spot
The q is generated
without considering j, n,
o.
18. The blind spots explanation
Absenting this
direction makes the
blind spots. These
spot number will
increase with the
layer number.
How about make a new
filters which still have this
direction?
Supposing:
The 3*3 kernel and
postulating 2 or 3 layers
19. Horizontal and vertical stack
The feature map can be separated into 2 parts according to the status of the rows:
1. The rows containing the predicted spot
2. The rows without the predicted spot
https://www.slideshare.net/suga93/conditional-image-generation-with-pixelcnn-decoders
masked
It’s equivalent to
use 2*3 kernel
*
*
This mask is due to we
will use the horizontal
stack information.
20. Construct the horizontal and vertical at the
same time
Vertical
stack
Horizontal
stack
*
*
+
New feature map
Horizontal stack provides the
final predicted output
Padding after
convolution
crop
This feature map will
see the “future spot”
without padding and
crop
21. What happen when vertical and horizontal
stacks merging
P P P P P P P P P
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18
1 2 3
13
22
1 2 3
13
21 22
= +
If we want to take
position 22 …
Blind spot disappear
(but we still use the same kernel shape)
22. The concept of conditional gated CNN
The input should be
weighted and normalized
using certain method.
The opening of the
gate can be also
decided by the input
The x is masked using the
same mask. This influences
the Wf and Wg
• The description can be also given as one-hot
encoding, h.
• x is thought as given from h
The h should be properly transform due to the shape should be the same to W*X.
The h can be also embedded use neural network, m, such that s = m(h)
V*s can be directly calculated without masking. The kernel size use the 1*1 in original
paper.
Vertical
stack
Horizontal
stack
Gate mechanism
The feature map will pass two functions.
The tanh will normalize the feature maps
while the sigmoid is the gate.
23. The whole postulated architecture v’ h’
v h
Vertical stack
Horizontal stack
output
26. Generative adversarial network
• The problem of generative model
• “structured things” seems having some certain “distribution”
• Human sentence : n + v + adj + n
• The generative model need to learn the “distribution”
• The types of generative model learning
• Select the most related object from database
• Just learn the similarity => like Deepblue
• Learn the distribution
input
Transform to vector
database output
input
Transform to vector
output
distribution
27. Generative adversarial network
• How to learn a distribution is a problem
Generative
model
Generated
samples
generate
These samples have certain pattern
Real
samples
Discriminator
Measure the
differences
Tell the generator how to modify the distribution
Give a random
vector
28. CycleGan
• The naïve GAN generated model depending on the random vector
• The user cannot decide what will be generated
• we can use the paired data to constrain the output
• Not every data have the paired data
• CycleGan solve the unpaired data problem
G
D
Image source d from H.Y. Lee, NTU
Paired data
29. Spatial cycle consistency is not enough
• Mode collapse is a
serious problem in
cycle GAN
• In (a), the generated
Obama has only some
pixels changing, but
they can generated
variated Trump.
• In (b), the similar
situation occurred.
• Self-adversirial attack
similar
different
31. The Recycle GAN
Xt Y’t
Gy
X’t
Gx
Cycle consistency
Dy Dx
True or False True or FalseCycle GAN
Video X Video Y
Px Py
P can use t frames to predict
(t+1) frame
Gx
Xt+1 Y’t+1 X’t+1
Cycle consistency
(Recycle loss)
Py
Px Maybe not needed
Recycle GAN
33. The guess discriminator
Xt Y’t
Gy
X’t
Gx
Cycle consistency
Dy Dx
True or False True or FalseCycle GAN
https://arxiv.org/pdf/1908.01517.pdf
Dguess
Which is the
constructed
sample
Guess discriminator
have the input and
the reconstructed
sample.
input
reconstructed
Dguess
Which one
is input?
在這步驟把
input和
reconstructed
sample 順序任
意排列,看D
能不能判別出
上下哪張是
input
但是實際使用上,
直接用noise的效果
就很不錯了。
34. How to choose the types of GAN
• “Are GANs Created Equal? A Large-Scale Study” (2017)
• https://arxiv.org/abs/1711.10337 (Google Brain)
結論:
燒完一堆花錢做實驗後,
還是原始版本最好。
37. Optical flow
D. Putcha et al, “Functional correlates of optic flow motion processing in Parkinson’s disease,” 2014
Optic flow stimuli illustration. Optic flow motion stimuli (A) simulate forward and backward
motion using dot fields that are expanding or contracting while rotating about a central focus.
Random motion (B) simulates non-coherent motion using dots moving at the same speeds used
in optic flow, but with random directions of movement. In the illustrations, the length of arrows
corresponds with dot speed, indication that dot speed increases with distance away from the
center.
https://mlatgt.blog/2018/09/06/learning-rigidity-and-scene-flow-estimation/
http://hammerbchen.blogspot.com/2011/10/art-of-optical-flow.html
41. Affine transformation
http://silverwind1982.pixnet.net/blog/post/160691705-affine-transformation
https://math.stackexchange.com/questions/884666/what-
are-differences-between-affine-space-and-vector-space
Affine space
If the point can map to affine space, this is
the “affine transformation”.
This transformation is supposed to be linear.
Affine space 2
Affine space 1
Affine space 2
Affine space 1
affine transformation
𝑋1
𝑌1
=
𝐴11 𝐴12
𝐴21 𝐴22
X
𝑋2
𝑌2
+
𝐵1
𝐵2
𝑋1
𝑌1
1
=
𝐴11 𝐴12 𝐵1
𝐴21 𝐴22 𝐵2
0 0 1
X
𝑋2
𝑌2
1
homogenerous coordinates
放大縮小 平移
如果放入三角函數還可以達到
“旋轉”的效果
𝑋1
𝑌1
1
=
cos(θ) −sin(θ) 𝐵1
sin(θ) cos(θ) 𝐵2
0 0 1
X
𝑋2
𝑌2
1
This is also inversed function.
Objective:
可以用一個神經網路學到這些affine
transformation的特性嗎?If Z > 1, the vector (X, Y, Z) will pass
another plane (which is not a sub-
vector space of each other) and give
a coordinates of (x, y)
(but the information of z would be loss)
42. non-volume preserving
You can find more detail from Dr. Hung-yi Lee’s Youtube channel
https://www.youtube.com/watch?v=uXY18nzdSsM
Volume preserving
The “determinate” of the vector
indicates the “volume”.
The characteristics:
1. The diagonal of Jacob matrix
between the functions are 1.
2. The relation between functions are
traceable.
What will the “non-volume preserving”
(NVP) do?
In such task based on the concepts of optical-flow
methods, the volume would not be so important.
But…
The characteristic of “traceable” is essential.
RealNVP: modifying the functions which keep the “invertible” characteristic
Identical
matrix
I don’t care
48. The process of Glow
製作Flow model時,就掌握三大原則:
1. Traceable
2. 想辦法打亂
3. 如果有channel,channel間必須有
相依性。(方便進行affine轉換)
RealNVP (2018)
Factor out
Scale block
…
Scale block
Z
Actnorm
1*1 Invertible conv
Affine coupling
Scale block
…
Y
Loss 內涵:
1. 在flow裡面,每一個點都有意義,因
此每個點都要算。
2. Normalizing flow的意思就是希望“把
output整到某種分布”,而我們常用的就
是常態分布。因此就把z跟y跟常態分佈
做maximum likelihood (MLE)。
3. 每次做轉換的時候並不希望有太大的”
體積轉變“(log determinant)
z
任何你喜歡的分
布,但大部分的
人會挑常態分佈
MLE
49. Actnorm layer
This layer try to scale the input into an “acceptable”
information for next layer.
• “acceptable” means the information have the 0 and 1
for mean and variance, respectively.
• This concept is similar to “batch normalization (BN).
Here, the mean and variance is get directly from the
input data.
• This is a special case of “data dependent initialization”
• This would be caused from BN is less traceable.
Data dependent initialization
https://arxiv.org/pdf/1511.06856.pdf (ICLR2016)
Layer 1
Layer 2
Layer k
….
Layer k-1
inputinputinput
Input of
Layer K
Layer k
output of
Layer K
控制這一層的比
重,使得output
也可以維持在
activation
function可以作
用的範圍
(控制抽樣的
distribution的
mean跟variance
即可)
Between layer normalization
原本都是假設前面幾層都是線性的
(supposed affine layer),但實際上不
是這樣。因此還是需要做進一步調整,
調整方式就是用多放不同的Batch進
去,然後估計不同樣本變化多大,在
進一步調整mean跟Variance(rk in the
algorithm2)。
Within layer initialization
inputinputinput inputinputinput
Data dependent initialization
https://arxiv.org/pdf/1511.06856.pdf (ICLR2016)