Spring 2022: Deep Learning and Its
Applications to Signal and Image Processing and Analysis, 361-21120
Tammy Riklin Raviv
Teaching assistants: Shaked Cohen
Time Wednesday, 14:00-17:00
Location Zoom, Online course
About the course
The class will cover a diverse set of topics in signal processing,
image analysis and computer vision and various Neural Network architectures. It will be an interactive course where we will discuss interesting topics on demand and latest research buzz.
The goal of the class is to learn about different domains of the
field, understand, identify and analyse the main challenges, what
works and what doesn't, as well as to identify interesting new
directions for future research.
Right after Passover, we will read about three papers every week. The
papers will be presented by the students in a seminar format. The
success of the discussion in class will thus be due to how prepared
the students come to class. Each student is expected to read at least
one paper for each class (and prepare a question or a comment for a discussion). In
addition, each student will present a paper in one
of the classes. There will be also talks by invited speakers and a
lab. class on Tensor Flow. The Last
class will be dedicated to final project presentation (about ten
minutes for each project). Details on the final project will be
published soon. The final project does not have to be necessarily
related to the paper presented in class.
Schedule, links and lecture notes
March 23, 2022 Course
overview, the biological and the artificial neurons, feature representation, common architectures
March 30, 2022 Loss functions, Optimization, Regularization
April 6, 2022
Tensor Flow - Shaked Cohen
April 13, 2022 BackProp, Optimization and Batch Normalization
April 27, 2022
Deep Residual Learning for Image Recognition
Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian
Sun
Presenters: Shai Aharon and Maxim Levit
You Only Look Once: Unified, Real-Time Object Detection
Authors: Joseph Redmon , Santosh Divvala, Ross Girshick , Ali Farhad
Presenters: Yoram Segal and Elad Sofer
FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff, Dmitry Kalenichenko, James Philbin
Presenters: Tomer Shaked and Ofek Finkelstein
May 11, 2022 Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Authors: Olaf Ronneberger, Philipp Fischer, Thomas Brox
Presenters: Maor Turner and Ori Feldman
YOLACT
Real-time Instance Segmentation
Daniel Bolya Chong Zhou Fanyi Xiao Yong Jae Lee
Presenters: Itamar Elmakias and Jameel Nassar
Mask R-CNN
Authors: Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick
Presenters: Yoel Bokobza, Oded George and Ziv Shahar
May 18, 2022 GANs
Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie
,Mehdi Mirza, Bing Xu, David Warde-Farley,Sherjil Ozair, Aaron Courville, Yoshua Bengio
Presenters: Yael Elkin and Ophir Gruteke
Image-to-Image Translation with Conditional Adversarial Networks
P. Isola, JY. Zhu, T. Zhou, A.A. Efros
Presenters: Ohad Shapira and Ran Greidi
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
J-Y Zhu, T. Park, P. Isola, A. A. Efros
Presenter:
SinGAN: Learning a Generative Model from a Single Natural Image
T. Rott Shaham, T. Dekel, T. Michaeli
Presenters: Itay Buchnik and Or Berebi
May 25, 2022 AE and more
Auto-Encoding Variational Bayes
Diederik P Kingma, Max Welling
Presenters: Raviv Ilani & Lotem Drori
Old Photo Restoration via Deep Latent Space
Translation
Ziyu Wan, Bo Zhang, Dongdong Chen, Pan Zhang, Dong Chen, Jing Liao, Fang Wen
Presenters: Elor and Michael
A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras, Samuli Laine, Timo Aila
Speakers:Priel Nissim and Carmi Shimon
June 1 , 2022 RNNs and Reinforcement Learning
A First Look at Music Composition using LSTM Recurrent Neural Networks
  Douglas Eck and Jurgen Schmidhuber
Presenters: Tal Itschakian & Rom Schilman
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
Authors: Itay Benou and Tammy Riklin Raviv
Guest lecture: Itay Benou
Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos
Authors: Assaf Arbelle, Shaked Cohen and Tammy Riklin Raviv
Guest lecture: Shaked Cohen
 
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
Presenters: Eliraz and Amichai
June 8, 2022 Transformers and Explainable AI
Attention Is All You Need
Authors: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
Presenters:Inbal Rimon, Camron, Guy
Attention on attention for image captioning
Authors: Lun Huang, Wenmin Wang, Jie Chen, Xiao-Yong Wei
Presenters:Hanna Kossowsky
Grad-CAM: Why did you say that?
Authors:Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell,
Devi Parikh and Dhruv Batra
Presenters:Reut Moshe and Daniel
June 15, 2022 Multiple Topics
"Zero Shot" Super-Resolution using Deep Internal Learning
Authors: Assaf Shocher, Nadav Cohen and Michal Irani
Presenter: Yakov Geltser
Adversarial domain adaptation for stable brain-machine interfaces
Authors: Ali Farshchian, Juan A. Gallego, Joseph P. Cohen, Yoshua Bengio, Lee E. Miller, Sara A. Solla
Presenter:Ophir Almagor
Mesh R-CNN
Authors: Georgia Gkioxari Jitendra Malik Justin Johnson
Presenters: Orel Ben Zaken and Omer Luxembourg
June 22, 2022 Offline class
June 29, 2022 Final project presentation day
Links
Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press
Online
Convolutional Neural Networks for Visual Recognition, Stanford Course
cs231n, see also
Lectures
Neural Networks for Machine Learning, Coursera course by Jeff Hinton
ConvNet Arithmetic
ConvNet Demo by Stanford
Tensor flow course
Tensor flow official site
Tensor flow code
Tensor flow playground
MATLAB for Deep Learning
Free Training – Deep Learning with MATLAB – No MATLAB license required
Student Presentations
Each student will need to present a paper in
class. Please refer to the list of papers below and note
time schedule to be presented. Ideas for
papers not in the list need approval. The presentation should be clear and practiced
and the student should read the assigned paper and related work in enough detail to be able to lead a discussion and answer questions.
A presentation should be roughly 30 minutes long
including time for questions and a discussion. Typically this is about
25 to 30 slides. In the presentation provide the citation to the paper you present and to any other related work you reference.
Deadline:The presentation should be handed in one day before
the class (or before if you want feedback).
Structure of presentation:
High-level overview with contributions,
Main motivation,
Clear statement of the problem,
Overview of the technical approach,
Strengths/weaknesses of the approach,
Overview of the experimental evaluation,
Strengths/weaknesses of evaluation,
Discussion: future directions, links to other works.
Further reading
ImageNet Classification with Deep Convolutional
Neural Networks
Alex Krizhevsky, Ilya Sutskever,Geoffrey
E. Hinton
Progressive Color Transfer with Dense Semantic Correspondences
Mingming He, Jing Liao, Dongdong Chen, Lu Yuan, Pedro V. Sander,
Learning Sparse Neural Networks through L0 regularization
Christos Louizos, Max Welling, Diederik P. Kingma
Supervised Uncertainty Quantification for
Segmentation with Multiple Annotations
Shi Hu, Daniel Worrall, Stefan Knegt, Bas Veeling, Henkjan Huisman, Max Welling
Learning Sparse Networks Using Targeted Dropout
Aidan N. Gomez,Ivan Zhang, Siddhartha Rao Kamalakara, Divyam Madaan,Kevin Swersky, Yarin Gal, Geoffrey E. Hinton
Dropout as a Bayesian Approximation:
Representing Model Uncertainty in Deep Learning
Yarin Gal, Zoubin Ghahramani
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
Pascal Vincent, Hugo Larochelle , Isabelle Lajoie , Yoshua Bengio , Pierre-Antoine Manzagol
Auto-Encoding Variational Bayes
Diederik P Kingma, Max Welling
YOLACT
Real-time Instance Segmentation
Daniel Bolya Chong Zhou Fanyi Xiao Yong Jae Lee
Deep Image Prior
Dmitry Ulyanov, Andrea Vedaldi,Victor Lempitsky
Objects that Sound
Relja Arandjelovi, Andrew Zisserman
Audio-Visual Scene Analysis with
Self-Supervised Multisensory Features
Andrew Owens, Alexei A. Efros
Learning Correspondence from the Cycle-consistency of Time
Xiaolong Wang, Allan Jabri, Alexei A. Efros
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
Abhishek Das, Satwik Kottur, Jose M.F. Moura, Stefan Lee, Dhruv Batra
Mesh R-CNN
Authors: Georgia Gkioxari Jitendra Malik Justin Johnson
Mask R-CNN
Authors: Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick
Spiking-YOLO: Spiking Neural Network for Real-time Object Detection
Authors: Seijoon Kim,Seongsik Park,Byunggook Na,Sungroh Yoon
Simultaneous Deep Transfer Across Domains and Tasks
Authors:Eric Tzeng, Judy Hoffman,
Trevor Darrell, Kate Saenko
Densely Connected Convolutional Networks
Authors:Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
Batch Normalization: Accelerating Deep Network Training by
Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy
Layer Normalization
Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
Dropout: A Simple Way to Prevent Neural Networks from
Overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex
Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
Dropout as a Bayesian Approximation:
Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani
Rich feature hierarchies for accurate object detection and semantic segmentation
L-C. Chen, G. Papandreou, I. Kokkinos,
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
U-net: Convolutional networks for biomedical
image segmentation
Ronneberger, O., Fischer, P., Brox, T.
Spectral Representations for
Convolutional Neural Networks
Oren Rippel, Jasper Snoek, Ryan P. Adams
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon , Santosh Divvala, Ross Girshick , Ali Farhad
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
Very Deep Convolutional Networks for large-scale image recognition
Karen Simonyan & Andrew Zisserman
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian
Sun
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander A. Alemi
Recurrent Neural Networks and Long Short Term Memory (LSTM) Networks
Unsupervised Learning of Video Representations using LSTMs
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor, Ivo Danihelka, Alex
Graves, Danilo Jimenez Rezende and Daan Wierstra
Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie
,Mehdi Mirza, Bing Xu, David Warde-Farley,Sherjil Ozair, Aaron Courville, Yoshua Bengio
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, Soumith Chintala
A Neural Algorithm of Artistic Style
Leon A. Gatys, Alexander S. Ecker,
Matthias Bethge
Multimodal learning with Deep Boltzman Machines
Nitish Srivastava and Ruslan Salakhutdinov
Auto-Encoding Variational Bayes
Diederik P Kingma,Max Welling
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
Pascal Vincent, Hugo Larochelle , Isabelle Lajoie , Yoshua Bengio , Pierre-Antoine Manzagol
Building High-level Features Using Large Scale Unsupervised Learning
Quoc V. Le, Marc Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado ,Jeff Dean, Andrew Y. Ng
Where Are They Looking?
Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba
FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff, Dmitry Kalenichenko, James Philbin
Learning to Compare Image Patches via Convolutional Neural Networks
Sergey Zagoruyko, Nikos Komodakis
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Jure Zbontar, Yann LeCun
Automatic Segmentation of MR Brain Images With
a Convolutional Neural Network
Pim Moeskops, Max A. Viergever, Adrienne M. Mendrik, Linda S. de Vries, Manon J. N. L.
Benders, and Ivana Isgum
Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks
Ozan Oktay, Wenjia Bai, Matthew
Lee, Ricardo Guerrero,Konstantinos Kamnitsas, Jose Caballero, Antonio
de Marvao,Stuart Cook, Declan ORegan
and Daniel Rueckert
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich
ImageNet Classification with Deep Convolutional
Neural Networks
Alex Krizhevsky, Ilya Sutskever,Geoffrey
E. Hinton
Monocular Object Instance Segmentation and Depth Ordering with CNNs
Ziyu Zhang, Alex Schwing, Sanja Fidler, Raquel Urtasun
Instance-Level Segmentation with Deep Densely Connected MRFs
Ziyu Zhang, Sanja Fidler, Raquel Urtasun
DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor,Ivo Danihelka, Alex Graves,
Danilo Jimenez Rezende, Daan Wierstra
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, Soumith Chintala
The Multiverse Loss for Robust Transfer Learning
Etai Littwin, Lior Wolf
Simultaneous Deep Transfer Across Domains and Tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
Mateusz Malinowski, Marcus Rohrbach, Mario Fritz
Auto-Encoding Variational Bayes
Diederik P Kingma, Max Welling
Tutorial: Bayesian Reasoning and Deep Learning
Shakir Mohamed
Deep learning and the information bottleneck principle
Naftali Tishby,Noga Zaslavsky
Unsupervised Learning of Video Representations using LSTMs
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, Christian
Szegedy
Recurrent Models of Visual Attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu
Generative Adversarial Nets
Ian J. Goodfellow,Jean Pouget-Abadie,Mehdi Mirza, Bing Xu, David Warde-Farley,Sherjil Ozair,Aaron Courville, Yoshua Bengio
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, Geoffrey E. Hinton
DeepFace: Closing
the Gap to Human-Level Performance in Face Verification
Yaniv Taigman, Ming Yang, Marc' Aurelio Ranzato, Lior Wolf
FaceNet: A Unified Embedding
for Face Recognition and Clustering
Florian Schroff, Dmitry Kalenichenko, James Philbin
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning
Oren Z. Kraus, Lei Jimmy Ba, Brendan Frey
Multi-input Cardiac Image Super-Resolution
Using Convolutional Neural Networks
Ozan Oktay, Wenjia Bai, Matthew Lee, Ricardo Guerrero,
Konstantinos Kamnitsas, Jose Caballero, Antonio de Marvao, Stuart Cook,
Declan ORegan, and Daniel Rueckert
We Are Humor Beings: Understanding and Predicting Visual Humor
Arjun Chandrasekaran, Ashwin K Vijayakumar,
Stanislaw Antol, Mohit Bansal, Dhruv Batra, C. Lawrence Zitnick, Devi
Parikh
A Neural Algorithm of Artistic Style
Leon A. Gatys, Alexander S. Ecker, Matthias
Bethge
A First Look at Music Composition using LSTM Recurrent Neural Networks
Douglas Eck, Jurgen Schmidhuber
Homework assignments
Homework assignments should be submitted via moodle using a single zip
file: "YourName-XX.zip" where XX is the assignment number.
Assignment #0 due to April 14, 2020
Assignment #1 due to April 23, 2020