GuokaiLiu / Noisy-Labels-Problem-CollectionLinks
This is a collection of Papers and Codes for Noisy Labels Problem.
☆63Updated 7 years ago
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- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- Gold Loss Correction☆87Updated 6 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆54Updated 6 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆85Updated 6 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆353Updated 6 years ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Updated last year
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- Meta-Learning based Noise-Tolerant Training☆126Updated 4 years ago
- Code for paper "Learning to Reweight Examples for Robust Deep Learning"☆269Updated 6 years ago
- Implementation of Temporal Ensembling for Semi-Supervised Learning by Laine et al. with tensorflow eager execution☆55Updated 5 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- code for paper Decoupling "when to update" from "how to update" [https://arxiv.org/abs/1706.02613]☆21Updated 7 years ago
- ☆129Updated 2 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- Tensorflow implementation of "Learning to Balance: Bayesian Meta-learning for Imbalanced and Out-of-distribution Tasks" (ICLR 2020 oral)☆101Updated 4 years ago
- Robust loss functions for deep neural networks (CVPR 2017)☆91Updated 5 years ago
- Official Pytorch Implementation for ICML'19 paper: Unsupervised Deep Learning by Neighbourhood Discovery☆156Updated 6 years ago
- The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks☆118Updated 5 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- Unofficial pytorch implementation of Born-Again Neural Networks.☆55Updated 4 years ago
- Code for ICLR 2019 Paper, "MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS"☆25Updated 2 years ago
- Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976☆25Updated 6 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks☆323Updated 2 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆128Updated 5 years ago
- Implementations of knowledge distillation and knowledge transfer models in neural networks.☆22Updated 6 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆290Updated 3 years ago