Spring 2017

  ECE 539: Advanced Topics in DSP: Deep Learning for Biometrics

    Time: Tu 8:40am-11:40am
    Location: SEC 212

    Instructor: Vishal M. Patel
    Email: vishal.m.patel@rutgers.edu
    Office: CoRE 508

    Office Hours: Thu 9:30-10:30, or by appointment

     Tentative Schedule
Prof. Patel
Intro to Biometrics
Face Recognition
An Introduction to Biometric Recognition by Jain et al.
Prof. Patel
Eigenfaces for recognition by Turk & Pentland

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection by Belhumeur et al.

Discriminant analysis for recognition of human face images by Etemand & Chellappa

A Tutorial on Support Vector Machines for Pattern Recognition by Burges

Robust Face Recognition via Sparse Representation by Wright et al.

Prof. Patel
Features: LBP, HOG, SIFT, Gabor
Intro to Deep Learning
Face Description with Local Binary Patterns: Application to Face Recognition by Ahonen et al.

Histograms of Oriented Gradients for Human Detection by Dalal & Triggs

Face Recognition by Elastic Bunch Graph Matching by Wiskott et al.

Distinctive Image Features from Scale-Invariant Keypoints by Lowe

He Zhang
Vishwanath Sindagi
Siamese Network
Visualizing CNNs
Intro to Caffe, Torch, Theano, TensorFlow, Paddle
ImageNet Classification with Deep Convolutional Neural Nets by Krivhevsky et al.

Visualizing and Understanding Convolutional Networks by Zeiler & Fergus

Learning a Similarity Metric Discriminatively, with Application to Face Verification by Chopra et al.

Dimensionality Reduction by Learning an Invariant Mapping by Hadsell et al.

Pramuditha Perera,
Puyang Wang,
Lidan Wang
De-noising autoencoders
Variational autoencoders
Deep Learning, Chapter 14

Extracting and Composing Robust Features with Denoising Autoencoders by Vincent et al.

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion by Vincent et al.

Tutorial on Variational Autoencoders by Doersch
Xing Di,
Mahdi Abavisani, Ahmed Al Hilli
Boltzmann Machines
Restricted Boltzmann Machines
Deep Belief Networks
Reducing the Dimensionality of Data with Neural Networks by Hinton & Salakhutdinov

Boltzmann Machines by Hinton

A Fast Learning Algorithm for Deep Belief Nets by Hinton et al.

A Practical Guide to Training Restricted Boltzmann Machines by Hinton
Siyuan Zhong
Yanyi Zhang
Jia Xue
Lulu Jiang
Deep Residual Networks
Very Deep Convolutional Neural Networks for Large-Scale Image Recognition by Simonyan & Zisserman

Network In Network by Lin et al.

Going Deeper with Convolutions by Szegedy et al.

Deep Residual Learning for Image Recognition by He et al.

Residual Networks Behave Like Ensembles of Relatively Shallow Networks by Veit et al.

Identity Mappings in Deep Residual Networks by He et al.

Aggregated Residual Transformations for Deep Neural Networks by Xie et al.

Spring break
Spring break Spring break
Wangzhe Chen
Hua Liu
Sirun Xu
Shuyu Lyu
Recurrent Neural Networks (RNNs)
Long Short Term Memory networks (LSTMs)
Long Short-Term Memory by Hochreiter & Schmidhuber

How to Construct Deep Recurrent Neural Networks by Pascanu et al.

Unsupervised Learning of Video Representations using LSTMs by Srivastava et al.

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling by Chung et al.

Generating Sequences With Recurrent Neural Networks by Graves

Long-term Recurrent Convolutional Networks for Visual Recognition and Description by Donahue et al.

Pixel Recurrent Neural Networks by van den Oord et al.

Anastasios Dimas
Yutong Gao
Shiyue Xu
Jianyu Zhang
Object Detection
Face Detection
Deep neural networks for object detection by Szegedy et al.

Rich feature hierarchies for accurate object detection and semantic segmentation by Girshick et al.

Spatial pyramid pooling in deep convolutional networks for visual recognition by He et al.

Fast R-CNN by Girshick

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Ren et al.

R-FCN: Object Detection via Region-based Fully Convolutional Networks by Dai et al.

You Only Look Once: Unified, Real-Time Object Detection by Redmon et al.

A deep pyramid deformable part model for face detection by Ranjan et al.

Ruiqi Lin
Zeyang Wang
Feeby M. Salib
Deep Face Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering by Schroff et al.

DeepFace: Closing the Gap to Human-Level Performance in Face Verification by Taigman et al.

Web-Scale Training for Face Identification by Taigman et al.

Deep Learning Face Representation from Predicting 10,000 Classes by Sun et al.

Deep Learning Face Representation by Joint Identification-Verification by Sun et al.

Deep Face Recognition by Parkhi et al.

Shang Yang
Talal Ahmed
Shengrui Zhou
Facial Landmark Localization
Facial Attributes
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks by Sermanet et al.

HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition by Ranjan et al.

An All-In-One Convolutional Neural Network for Face Analysis by Ranjan et al.

Deep Learning Face Attributes in the Wild by Liu et al.

Face Alignment by Local Deep Descriptor Regression by Kumar et al.

Facial Landmark Detection by Deep Multi-task Learning by Zhang et al.

Jason T Occidental
Hansi Liu
Peiyao Yang
Generative Adversarial Networks (GANs)
Spatial Transformer Networks
Fully Convolutional Networks
Generative Adversarial Nets by Goodfellow et al.

NIPS 2016 Tutorial: Generative Adversarial Networks by Goodfellow

Spatial Transformer Networks by Jaderberg et al.

Fully Convolutional Networks for Semantic Segmentation by Long et al.

Michael G Soskind
Yuese Wang
Jamal Alasadi
Neural Turing Machines
Orderless Set Network
Meta Learning
Memory Networks
Neural Turing Machines by Graves et al.

Order Matters: Sequence to sequence for sets by Vinyals et al.

Memory networks Weston et al.

End-To-End Memory Networks by Sukhbaatar et al.

Meta-Learning with Memory-Augmented Neural Networks by Santoro et al.

Neural Aggregation Network for Video Face Recognition by Yang et al.


Deep Learning, by Ian Goodfellow and Yoshua Bengio and Aaron Courville
   Neural Networks and Deep Learning, by Michael Nielsen