# Label Noise and Anomaly Detection

## Learning on Noisy/Corrupted/Imbalanced Labels
- GitHub resources: [[subeeshvasu/Awesome-Learning-with-Label-Noise]](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise).
- [2017 ICCV] **Learning from Noisy Labels with Distillation**, [[paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Learning_From_Noisy_ICCV_2017_paper.pdf), [[bibtex]](/Bibtex/Learning%20from%20Noisy%20Labels%20with%20Distillation.bib), sources: [[raingo/yfcc100m-entity]](https://github.com/raingo/yfcc100m-entity).
- [2017 ICML] **A Closer Look at Memorization in Deep Networks**, [[paper]](http://proceedings.mlr.press/v70/arpit17a/arpit17a.pdf), [[bibtex]](/Bibtex/A%20Closer%20Look%20at%20Memorization%20in%20Deep%20Networks.bib).
- [2017 ICLR] **Training Deep Neural-networks Using a Noise Adaptation Layer**, [[paper]](https://openreview.net/pdf?id=H12GRgcxg), [[bibtex]](/Bibtex/Training%20Deep%20Neural-networks%20Using%20a%20Noise%20Adaptation%20Layer.bib), sources: [[udibr/noisy_labels]](https://github.com/udibr/noisy_labels).
- [2017 NIPS] **Decoupling "when to update" from "how to update"**, [[paper]](https://papers.nips.cc/paper/6697-decoupling-when-to-update-from-how-to-update.pdf), [[supplementory]](https://papers.nips.cc/paper/6697-decoupling-when-to-update-from-how-to-update), [[bibtex]](/Bibtex/Decoupling%20when%20to%20update%20from%20how%20to%20update.bib), sources: [[emalach/UpdateByDisagreement]](https://github.com/emalach/UpdateByDisagreement).
- [2018 ICML] **MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels**, [[paper]](http://proceedings.mlr.press/v80/jiang18c/jiang18c.pdf), [[supplementory]](http://proceedings.mlr.press/v80/jiang18c/jiang18c-supp.pdf), [[bibtex]](/Bibtex/MentorNet.bib), sources: [[google/mentornet]](https://github.com/google/mentornet).
- [2018 NeurIPS] **Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels**, [[paper]](https://papers.nips.cc/paper/8072-co-teaching-robust-training-of-deep-neural-networks-with-extremely-noisy-labels.pdf), [[bibtex]](/Bibtex/Co-teaching.bib), sources: [[bhanML/Co-teaching]](https://github.com/bhanML/Co-teaching).
- [2018 NeurIPS] **Masking: A New Perspective of Noisy Supervision**, [[paper]](https://papers.nips.cc/paper/7825-masking-a-new-perspective-of-noisy-supervision.pdf), [[bibtex]](/Bibtex/Masking%20-%20A%20New%20Perspective%20of%20Noisy%20Supervision.bib), sources: [[bhanML/Masking]](https://github.com/bhanML/Masking).
- [2018 NeurIPS] **Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise**, [[paper]](https://arxiv.org/pdf/1802.05300.pdf), [[bibtex]](/Bibtex/Using%20Trusted%20Data%20to%20Train%20Deep%20Networks%20on%20Labels%20Corrupted%20by%20Severe%20Noise.bib), sources: [[mmazeika/glc]](https://github.com/mmazeika/glc).
- [2018 JMLR] **A Theory of Learning with Corrupted Labels**, [[paper]](http://www.jmlr.org/papers/volume18/16-315/16-315.pdf), [[bibtex]](/Bibtex/A%20Theory%20of%20Learning%20with%20Corrupted%20Labels.bib).
- [2019 TIP] **Deep Learning From Noisy Image Labels With Quality Embedding**, [[paper]](/Documents/Papers/Deep%20Learning%20From%20Noisy%20Image%20Labels%20With%20Quality%20Embedding.pdf), [[bibtex]](/Bibtex/Deep%20Learning%20From%20Noisy%20Image%20Labels%20With%20Quality%20Embedding.bib).
- [2019 NeurIPS] **Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting**, [[paper]](https://arxiv.org/pdf/1902.07379.pdf), [[bibtex]](/Bibtex/Meta-Weight-Net%20-%20Learning%20an%20Explicit%20Mapping%20For%20Sample%20Weighting.bib), sources: [[xjtushujun/meta-weight-net]](https://github.com/xjtushujun/meta-weight-net).

## Anomaly Detection
- [2017 IPMI] **Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery**, [[paper]](https://arxiv.org/pdf/1703.05921.pdf), [[bibtex]](/Bibtex/Unsupervised%20Anomaly%20Detection%20with%20Generative%20Adversarial%20Networks%20to%20Guide%20Marker%20Discovery.bib), sources: [[LeeDoYup/AnoGAN]](https://github.com/LeeDoYup/AnoGAN), [[tkwoo/anogan-keras]](https://github.com/tkwoo/anogan-keras), [[yjucho1/anoGAN]](https://github.com/yjucho1/anoGAN).
- [2018 ICML] **Deep One-Class Classification**, [[paper]](http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf), [[supplementary]](http://proceedings.mlr.press/v80/ruff18a/ruff18a-supp.pdf), [[bibtex]](/Bibtex/Deep%20One-Class%20Classification.bib), sources: [[lukasruff/Deep-SVDD]](https://github.com/lukasruff/Deep-SVDD).
- [2019 TIFS] **AnomalyNet: An Anomaly Detection Network for Video Surveillance**, [[paper]](/Documents/Papers/AnomalyNet%20-%20An%20Anomaly%20Detection%20Network%20for%20Video%20Surveillance.pdf), [[bibtex]](/Bibtex/AnomalyNet%20-%20An%20Anomaly%20Detection%20Network%20for%20Video%20Surveillance.bib).