haochenglouis / cores
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)
☆34Updated 4 years ago
Alternatives and similar repositories for cores:
Users that are interested in cores are comparing it to the libraries listed below
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆36Updated 4 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆76Updated 3 years ago
- Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)☆103Updated 3 years ago
- Adjust Decision Boundary for Class Imbalanced Learning☆19Updated 4 years ago
- [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang☆63Updated 3 years ago
- Official Implementation of Robust Training under Label Noise by Over-parameterization☆64Updated 2 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆126Updated 5 years ago
- PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"☆71Updated 4 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆48Updated last year
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆39Updated 3 years ago
- NeurIPS'2020: Part-dependent Label Noise: Towards Instance-dependent Label Noise☆60Updated 4 years ago
- ☆29Updated 2 years ago
- Meta Label Correction for Noisy Label Learning☆85Updated 2 years ago
- "Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" (NeurIPS 2020).☆50Updated 4 years ago
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆106Updated 4 years ago
- Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…☆53Updated 3 years ago
- Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)☆91Updated 4 years ago
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆91Updated 3 years ago
- Learning with Noisy Labels by adopting a peer prediction loss function.☆35Updated 5 years ago
- ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning☆77Updated last year
- MoPro: Webly Supervised Learning☆87Updated 4 years ago
- [AAAI 21] Utilizing meta-learning to correct the noisy labels.☆14Updated 3 years ago
- [ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization☆40Updated 3 years ago
- Official implementation for: "Multi-Objective Interpolation Training for Robustness to Label Noise"☆39Updated 2 years ago
- Code for our ECCV paper -- "Learning to Balance Specificity and Invariance for In and Out of Domain Generalization"☆56Updated 4 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆78Updated 4 years ago
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆36Updated 3 years ago