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aaai17-cdq

The implementation of AAAI-17 paper "Collective Deep Quantization of Efficient Cross-modal Retrieval"

adaptive-exploration-for-unsupervised-person-re-identification

code of our work : Adaptive Exploration for Unsupervised Person Re-Identification

ade

The code of ADE

adnet

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (CVPR 2017)

affinity-loss

Unofficial implementation of "Max-margin Class Imbalanced Learning with Gaussian Affinity"

attention-module

Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"

attentionn

All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.

awesome-cbir-papers

📝Awesome and classical image retrieval papers

awesome-contrastive-self-supervised-learning

A comprehensive list of awesome contrastive self-supervised learning papers.

awesome-realistic-semi-supervised-learning

An awesome paper list of Semi-Supervised Learning under realistic settings.

awesome-semi-supervised-learning

:scroll: A curated list of awesome semi-supervised learning methods & papers.

awesome_video_person_reid

papers collection and understanding for video person re-identification

bayesian-methods-for-ml

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.

bgan

c3d-tensorflow-1

Action recognition with C3D network implemented in tensorflow

c3d-tensorflow-slim

A simple Tensorflow code for C3D

cassandraclient

connect cassandra database

cbam.pytorch

Non-official implement of Paper：CBAM: Convolutional Block Attention Module

cbam_pytorch

《CBAM: Convolutional Block Attention Module》 pytorch实现

changingfond.github.io

Personal blog based on Hexo

cifar10dataset

Creat your own dataset with the similar format with CIFAR10 in python version.

cnn-rnn-a-unified-framework-for-multi-label-image-classification

Paper implementation

comatch

Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

consistencyssl

This repository contains the code for our paper "Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization"

contrastive-clustering

Code for the paper "Contrastive Clustering" (AAAI 2021)

contrastive_learning

contrastive_loss

Experiments with supervised contrastive learning methods with different loss functions

cosonet

The source code for the paper: Yirong Mao, Ruiping Wang, Shiguang Shan, Xilin Chen. COSONet: Compact Second-Order Network for Video Face Recognition. ACCV 2018

cvpr17-dvsq

The implementation of CVPR-17 paper "Deep Visual-Semantic Quantization of Efficient Image Retrieval"