Recognition, Classification, Clustering, Retrieval

Yichun Shi

MSU Biometrics Lab

  • [x] DocFace+: ID Document to Selfie Matching
  • [ ] Improving Face Recognition by Exploring Local Features with Visual Attention
  • [ ] Face Clustering: Representation and Pairwise Constraints
  • [ ] Face Recognition: Primates in the Wild
  • [x] RegularFace: Deep Face Recognition via Exclusive Regularization
  • [x] UniformFace, Equidistributed Representation for Face Recognition
  • [x] Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)
  • [ ] Decorrelated Adversarial Learning for Age-Invariance Face Recognition
  • [ ] Center Loss
  • [ ] Learning Invariant Deep Representation for NIR-VIS Face Recognition
  • [ ] Transferring deep representation for NIR-VIS heterogeneous face recognition

Detection, Segmentation

  • [ ] YOLO
  • [ ] YOLO V2
  • [ ] YOLO V3
  • [ ] SSD
  • [x] Feature Pyramid Networks for Object Detection
  • [ ] Non-Local Neural Networks

Network Architecture

  • [x] Deep High-Resolution Representation Learning for Human Pose Estimation (HRNet)
  • [ ] SENet
  • [ ] High-Resolution Representations for Labeling Pixels and Regions (HRNet V2)
  • [ ] PolyNet: A Pursuit of Structural Diversity in Very Deep Networks (PolyNet)

Optimization / Training Scheme

  • [ ] Adversarial Examples Are Not Bugs, They Are Features

Related blog, thread / discussion.

Probability Theory, Machine Learning

  • [ ] Discriminating Between the Normal and the Lablace Distributions
  • [ ] Reconciling Modern Machine Learning and the Bias-Variance Trade-off
  • [ ] To understand deep learning we need to understand kernel learning
  • [ ] Introduction to RKHS, and some simple kernel algorithms
  • [ ] A Primer on Reproducing Kernel Hilbert Spaces
  • [ ] A Training Algorithm for Optimal Margin Classi􏰀ers

results matching ""

    No results matching ""