HanxunH / Active-Passive-LossesLinks
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
☆141Updated last year
Alternatives and similar repositories for Active-Passive-Losses
Users that are interested in Active-Passive-Losses are comparing it to the libraries listed below
Sorting:
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆130Updated 6 years ago
- When Does Label Smoothing Help?_pytorch_implementationimp☆126Updated 6 years ago
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆190Updated 5 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆300Updated 2 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆79Updated 5 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆127Updated 2 years ago
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆171Updated 4 years ago
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆129Updated 8 months ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆139Updated 5 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆45Updated 3 years ago
- Improving Calibration for Long-Tailed Recognition (CVPR2021)☆153Updated 4 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆90Updated 6 years ago
- Regularizing Class-wise Predictions via Self-knowledge Distillation (CVPR 2020)☆109Updated 5 years ago
- ☆131Updated 3 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆140Updated 6 years ago
- A collection of awesome things about mixed sample data augmentation☆132Updated 5 years ago
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆41Updated 4 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆155Updated 5 years ago
- Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning☆101Updated last year
- (L2ID@CVPR2021, TNNLS2022) Boosting Co-teaching with Compression Regularization for Label Noise☆51Updated 2 years ago
- "In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Riz…☆236Updated 2 years ago
- PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"☆71Updated 4 years ago
- SKD : Self-supervised Knowledge Distillation for Few-shot Learning☆102Updated 2 years ago
- [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training☆130Updated 4 years ago
- Pytorch Implementation of Domain Generalization Using a Mixture of Multiple Latent Domains☆105Updated 4 years ago
- Addressing Failure Prediction by Learning Model Confidence☆173Updated 3 years ago
- Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"☆40Updated 3 years ago
- Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)☆95Updated 4 years ago
- [CVPR 2021] Adaptive Consistency Regularization for Semi-Supervised Transfer Learning☆106Updated 4 years ago
- NLNL: Negative Learning for Noisy Labels☆104Updated 6 years ago