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School of Computing

Deep Learning for Vision

NUS SoC, 2017/2018, Semester I
CS 6101 - Exploration of Computer Science Research

Last updated: July 3, 2018 - This is section of the course is over. Please visit the website for the current iteration of the course.
September 7, 2017 - Updated venue and assignments.
July 5, 2017 - Initial port of last year's home page.

This is a section of the CS 6101 Exploration of Computer Science Research at NUS. CS 6101 is a 4 modular credit pass/fail module for new incoming graduate programme students to obtain background in an area with an instructor's support. It is designed as a "lab rotation" to familiarize students with the methods and ways of research in a particular research area. The lab rotation is hosted by the Web IR / NLP Group (WING) at NUS, led by Min-Yen Kan.

Our section will be conducted as a group seminar, with class participants nominating themselves and presenting the materials and leading the discussion. It is not a lecture-oriented course and not as in-depth as Fei-Fei Li et al.'s original course at Stanford, and hence is not a replacement, but rather a class to spur local interest in Deep Learning for Vision. Unlike the original course, we do not require you to do projects, assignments or homework, although those that attempt this will be encouraged to present in the project showcase at the end of the course to the general public.

This "course" is offered twice, for Session I (Weeks 3-7)and Session II (Weeks 8-13), although it is clear that the course is logically a single course that builds on the first half. Nevertheless, the material should be introductory and should be understandable given some prior study.

A discussion group will be on Slack . Students and guests, please login when you are free. If you have a,,,, or email address you can create your Slack account for the group discussion without needing an invite.

Meeting Venue and Time

18:00-20:00, Tuesdays for both Sessions I (Weeks 3-7) and Session II (Weeks 8-13).

Updated Venue is Active Learning Lab (COM1 #B1-03).

For directions to NUS School of Computing (SoC) and COM1: please read the directions here, to park in CP15 or take the shuttle bus to SoC.

Please eat before the course or during (we don't mind -- like a brown-bag seminar series).


Welcome. If you are an external visitor and would like to join us, please email Kan Min-Yen to be added to the class role. Guests from industry, schools and other far-reaching places in SG welcome, pending space and time logistic limitations. The more, the merrier.

External guests will be listed here in due course once the course has started. Please refer to our Slack after you have been invited for the most up-to-date information.

NUS (Postgraduate): Session I (Weeks 3-7): Siddharth Aravindan, Devamanyu Hazarika, Nguyen Van Hoang, Xie Yaqi, Ziwei Xu, Yew Zi Jian

NUS (Postgraduate): Session II (Weeks 8-13): Cheng Chen, Xinyu Chen,Remmy A. M. Zen, Ridi Hossain, Chengxi Xue, Meng-Jiun Chiou

Other NUS: Wesley Chui Lui Goi, Jethro Kuan, Nandha Kumar, Kyaw Zaw Lin, Joel Lee, Panpan Qi, Krishnendu Sanyal, Shivshankar Umashankar, Ming Rui Wang

WING: Muthu Kumar Chandrasekaran, Divish Dayal, Min-Yen Kan, Animesh Prasad

Guests: Ming Liang Ang, Nipun Batra, Wesley Chui Lui Goi, Canh Tran Duy, Christabella Irwanto, Siow Meng Low, Jishnu Mohan, Nyan Tun Zaw, Karthik Raja Periasamy, Sherly Sherly, Kok Keong Teo, Shivshankar Umashankar, Sriram Vaikundam, Daniel Wong


We're happy to report that the Stanford course CS231n: Convolutional Neural Networks for Visual Recognition, taught by Fei-Fei Li, Justin Johnson and Serena Yeung has agreed to let us run an iteration of their course at NUS. Please refer to their page for the detailed syllabus.

Please note that the original course contains additional lectures that are prerequisites to the materials that we are using. Our course starts on Lecture 4, but participants may want to start with the pre-flight Lectures 1-3 that we may cover optionally before the course formally starts. Please check in Slack for more details.



Video Conference Room
(COM1 #02-VCRm)
Thursdays 6-8pm

Weeks 1-2


17 Aug
24 Aug

Course Introduction: Computer vision overview
Historical context
Course logistics

Image Classification: The data-driven approach
K-nearest neighbor
Linear classification I

Loss Functions and Optimization: Linear classification II
Higher-level representations, image features
Optimization, stochastic gradient descent
Presenters: Min
Session I


Weeks 3-7
ALL (COM1 #B1-03)


Week 3 (29 Aug)
Introduction to Neural Networks: Backpropagation
Multi-layer Perceptrons
The neural viewpoint
Presenters: Min
Week 4 (5 Sep)
Convolutional Neural Networks:History
Convolution and pooling
ConvNets outside vision
Presenters/Questioners: Devamanyu Hazarika, Siddharth Aravindan, Ming Rui Wang / Xie Yaqi, Kenneth Tan, Kok Keong Teo
Week 5 (12 Sep)
Training Neural Networks, part I:Activation functions, initialization, dropout, batch normalization
Presenters/Questioners: Jishnu Mohan, Yew Zi Jian, Xie Yaqi, Le Trung Hieu / Kyaw Zaw Lin, Wesley Chui Lui Goi, Shivshankar Umashankar
Week 6 (19 Sep)
Training Neural Networks, part II:Update rules, ensembles, data augmentation, transfer learning
Presenters/Questioners: Sherly, Kok Keong Teo, Ziwei Xu, Panpan Qi / Yushan Ma, Siddharth Aravindan, Yew Zi Jian, Jishnu Mohan
Recess Week (26 Sep)
Deep Learning Software:Caffe, Torch, Theano, TensorFlow, Keras, PyTorch, etc
Presenters/Questioners: Nguyen Van Hoang, Divish Dayal, Wesley Chui Lui Goi, Shivshankar Umashankar / Panpan Qi, Ming Rui Wang, Nandha Kumar, Nipun Batra, Sherly
Week 7 (3 Oct)
CNN Architectures: AlexNet, VGG, GoogLeNet, ResNet, etc
Presenters/Questioners: Yushan Ma, Sriram Vaikundam, Karthik Raja Periasamy, Canh Tran Duy / Devamanyu Hazarika, Le Trung Hieu, Nguyen Van Hoang, Ziwei Xu
Session II


Weeks 8-13
ALL (COM1 #B1-03)


Week 8 (10 Oct)
Recurrent Neural Networks: RNN, LSTM, GRU
Language modeling
Image captioning, visual question answering
Soft attention
Presenters/Questioners: Xinyu Chen, Krishnendu Sanyal, Nandha Kumar, Meng-Jiun Chiou, Ridi Hossain / Mohammed Haroon Dupty, Jinho Lee, Jeanne Choo, Raymond Chan
Week 9 (17 Oct)
Detection and Segmentation: Semantic segmentation
Object detection
Instance segmentation
Presenters/Questioners: Cheng Chen, Siow Meng Low, Remmy A. M. Zen, Daniel Wong / Sriram Vaikundam, Karthik Raja Periasamy, Canh Tran Duy, Krishnendu Sanyal, Xinyu Chen
Week 10 (24 Oct)
Visualizing and Understanding :Feature visualization and inversion
Adversarial examples
DeepDream and style transfer
Presenters/Questioners: Chengxi Xue, Dechao Chen, Raymond Chan, Christabella Irwanto, Jethro Kuan / Ridi Hossain, Daniel Wong, Siow Meng Low, Divish Dayal

Visenze Talk
Week 11 (31 Oct)
Generative Models: PixelRNN/CNN
Variational Autoencoders
Generative Adversarial Networks
Presenters/Questioners: Jeanne Choo, Ming Liang Ang, Mohammed Haroon Dupty, Kenneth Tan / Christabella Irwanto, Jethro Kuan, Tram Anh Nguyen, Chengxi Xue

CS 6101 Social at O Bar
Week 12 (7 Nov)
Deep Reinforcement Learning: Policy gradients, hard attention
Q-Learning, Actor-Critic
Presenters/Questioners: Nyan Tun Zaw, Tram Anh Nguyen, Kyaw Zaw Lin, Jinho Lee, Arun Raja / Remmy A. M. Zen, Ming Liang Ang, Joel Lee, Yexin Zhu, Cheng Chen, Xiao Ma
Week 13 (14 Nov)
Real-World Use
Presenters/Questioners: Yexin Zhu, Xiao Ma, Raymond Chan, Joel Lee, Nipun Batra / Meng-Jiun Chiou, Nyan Tun Zaw, Dechao Chen, Arun Raja

Student Projects

Student projects are required for all external guests to the course, to students in the course with conflicting lecture timings. All students in the course are highly encouraged to take on a project to gain practical experience in tandem with the class lecture. All student projects can be done in any sized team, and will be featured in the 11th SoC Term Project Showcase (STePS).

The below listing is tentative, please refer to the Slack group or to the STePS website for authoritative information.

  • Ang Mingliang: Deepore: Deep learning for base calling MinION reads
  • Christabella Irwanto / Jethro: A Neural Network Approach to Product Similarity and Pricing Suggestions - 1st Place winner at 11th STePS!
  • Kok Keong Teo: How Transferrable are Features in Deep Neural Networks?
  • Joel Lee / Le Trung Hieu: Yet Another attempt on the CDiscount Kaggle Competition
  • Swee Kiat Lim: Deep Reinforcement Learning with StarCraft II - 2nd Place winner at 11th STePS!
  • Yunshan Ma / Ziwei Xu: E-Commerce Product Classification on both Image and Text Information
  • Tram Anh Nguyen: Right Whale Challenge
  • Kenneth Tan: Drug Guru
  • Kok Keong Teo: How Transferrable are Features in Deep Learning Model?
  • Siow Meng: ImageNet Object Detection
  • Shivshankar Umashankar / Wesley Goi: Multi-class classification of e-commerce products
  • Suchun Wong: Bag-Of-Words Representation of Instances from Faster-RCNN
  • Deric Yeak: FaceLog Mobile Solution - 3rd Place winner at 11th STePS!

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