chenghao's repositories
Towards-Generalized-and-Efficient-Metric-Learning-on-Riemannian-Manifold
[IJCAI 2018] "Towards Generalized and Efficient Metric Learning on Riemannian Manifold"
awesome-anomaly-detection
A curated list of awesome anomaly detection resources
Awesome-Few-Shot-Class-Incremental-Learning
Awesome Few-Shot Class-Incremental Learning
CEC-CVPR2021
Pytorch code for CVPR2021 paper "Few-Shot Incremental Learning with Continually Evolved Classifiers"
chengcv.github.io
chenghao‘s homepage
constrained-FSCIL
PyTorch Implementation of the CVPR'22 Paper "Constrained Few-shot Class-incremental Learning"
DeepEMD
Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Exposure_Correction
Project page of the paper "Learning Multi-Scale Photo Exposure Correction" (CVPR 2021).
fast-MPN-COV
@CVPR2018: We propose a fast MPN-COV method for computing matrix square root normalization, which is very efficient, scalable to multiple-GPU configuration, while enjoying matching performance with MPN-COV
FEAT
The code repository for "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions"
few-shot
Repository for few-shot learning machine learning projects
few-shot-meta-baseline
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021
ganomaly
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
MatchingNetworks
This repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset
meta-learning-lstm
This repo contains the source code accompanying a scientific paper with the same name.
Prototypical-Networks-for-Few-shot-Learning-PyTorch
Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch
pymanopt
Python toolbox for optimization on manifolds with support for automatic differentiation
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration