- GitHub resources: [subeeshvasu/Awesome-Learning-with-Label-Noise].
- [2017 ICCV] Learning from Noisy Labels with Distillation, [paper], [bibtex], sources: [raingo/yfcc100m-entity].
- [2017 ICML] A Closer Look at Memorization in Deep Networks, [paper], [bibtex].
- [2017 ICLR] Training Deep Neural-networks Using a Noise Adaptation Layer, [paper], [bibtex], sources: [udibr/noisy_labels].
- [2017 NIPS] Decoupling "when to update" from "how to update", [paper], [supplementory], [bibtex], sources: [emalach/UpdateByDisagreement].
- [2018 ICML] MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels, [paper], [supplementory], [bibtex], sources: [google/mentornet].
- [2018 NeurIPS] Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels, [paper], [bibtex], sources: [bhanML/Co-teaching].
- [2018 NeurIPS] Masking: A New Perspective of Noisy Supervision, [paper], [bibtex], sources: [bhanML/Masking].
- [2018 NeurIPS] Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise, [paper], [bibtex], sources: [mmazeika/glc].
- [2018 JMLR] A Theory of Learning with Corrupted Labels, [paper], [bibtex].
- [2019 TIP] Deep Learning From Noisy Image Labels With Quality Embedding, [paper], [bibtex].
- [2019 NeurIPS] Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting, [paper], [bibtex], sources: [xjtushujun/meta-weight-net].
- [2017 IPMI] Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery, [paper], [bibtex], sources: [LeeDoYup/AnoGAN], [tkwoo/anogan-keras], [yjucho1/anoGAN].
- [2018 ICML] Deep One-Class Classification, [paper], [supplementary], [bibtex], sources: [lukasruff/Deep-SVDD].
- [2019 TIFS] AnomalyNet: An Anomaly Detection Network for Video Surveillance, [paper], [bibtex].