hjimce / O2U-NetLinks
paper "O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks" code
☆78Updated 2 years ago
Alternatives and similar repositories for O2U-Net
Users that are interested in O2U-Net are comparing it to the libraries listed below
Sorting:
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆170Updated 4 years ago
- Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification☆82Updated 4 years ago
- Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112☆186Updated 4 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- [NeurIPS 2020] Balanced Meta-Softmax for Long-Tailed Visual Recognition☆140Updated 3 years ago
- ☆14Updated 6 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆137Updated 11 months ago
- Ranking-based-Instance-Selection☆32Updated 4 years ago
- [CVPR 2021] Adaptive Consistency Regularization for Semi-Supervised Transfer Learning☆104Updated 3 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Updated 4 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
- Learning Attentive Pairwise Interaction for Fine-Grained Classification, AAAI-2020☆128Updated 3 years ago
- Few-Shot Learning with Global Class Representations☆74Updated 3 years ago
- [MentorMix] "Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels" implemented in the PyTorch version.☆18Updated 4 years ago
- The official code for the paper "Delving Deep into Label Smoothing", IEEE TIP 2021☆81Updated 2 years ago
- When Does Label Smoothing Help?_pytorch_implementationimp☆124Updated 5 years ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆139Updated 4 years ago
- [CVPRW 2020] Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification☆41Updated 3 years ago
- Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)☆118Updated 3 years ago
- PyTorch implementations of "Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning" (NeurIPS2020…☆31Updated 4 years ago
- Pytorch implementation for Deep Self-Learning From Noisy Labels☆33Updated 5 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆127Updated last month
- Meta-Learning based Noise-Tolerant Training☆125Updated 4 years ago
- Reimplementation of Mutual-Channel Loss for Fine-Grained Image Classification.☆37Updated 5 years ago
- implement of paper 'Probabilistic End-to-end Noise Correction for Learning with Noisy Labels'☆16Updated 5 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- PyTorch Implementation for SoftTriple Loss☆207Updated 11 months ago