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2021-cv-surveys

2021 年，计算机视觉相关综述。包括目标检测、跟踪........

active-passive-losses

Code for ICML2020 paper [“Normalized Loss Functions for Deep Learning with Noisy Labels"] https://arxiv.org/abs/2006.13554

algorithm-base

专门为刚开始刷题的同学准备的算法基地，没有最细只有更细，立志用动画将晦涩难懂的算法说的通俗易懂！

area-under-the-margin-ranking

Implementation of the paper Identifying Mislabeled Data using the Area Under the Margin Ranking: https://arxiv.org/pdf/2001.10528v2.pdf

augmentation-for-lnl

[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".

awesome-knowledge-distillation

Awesome Knowledge Distillation

awesome-learning-with-label-noise

A curated list of resources for Learning with Noisy Labels

bic

pytorch implementation of Large Scale Incremental Learning

computervision-recipes-

微软开源计算机视觉专题库，含分类、检测、分割、关键点、跟踪、动作识别等主流方向

contest

contest for chreos

contrastive-classification-keras

Implementation of self-supervised image-level contrastive pretraining methods using Keras.

crust

[NeurIPS 2020] Coresets for Robust Training of Neural Networks against Noisy Labels

deep-learning-with-tensorflow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

elr

Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels

google-research

Google Research

hello-algorithm

🌍 针对小白的算法训练 | 包括四部分：①.算法基础 ②.力扣图解 ③.大厂面经 ④.CS_汇总 | 附：1、千本开源电子书 2、百张技术思维导图（项目花了上百小时，希望可以点 star 支持，🌹感谢~）

idn

AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

imbalanced-semi-self

[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

influence_subsampling

Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020

knowledge-distillation-zoo

l_dmi

Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"

labelrelaxation

Supplementary material and code for the novel label relaxation approach as presented at AAAI21 by Julian Lienen and Eyke Hüllermeier.

lantern-binaries

Lantern installers binary downloads.

learning-from-rules

Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)

limit-label-memorization

Improving generalization by controlling label-noise information in neural network weights.

mlnt

Meta-Learning based Noise-Tolerant Training

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

mslg_noisy_label

Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.

noise_fairlearn

Code for Noise-tolerant fair classification

noisystudent

Code for Noisy Student Training. https://arxiv.org/abs/1911.04252