GarrettLee / label_noise_correction
Implementation of paper: Making Deep Neural Network Robust to Label Noise: a Loss Correction Approach.
☆23Updated last year
Related projects ⓘ
Alternatives and complementary repositories for label_noise_correction
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆83Updated 5 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- Code for CVPR 2019 paper Label Propagation for Deep Semi-supervised Learning☆115Updated 4 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆220Updated 4 years ago
- Joint Optimization Framework for Learning with Noisy Labels☆45Updated 6 years ago
- Adaptive Cross-Modal Few-shot learning OSS code. This is a ServiceNow Research project that was started at Element AI.☆65Updated 2 years ago
- Meta-Learning based Noise-Tolerant Training☆123Updated 4 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- LaSO: Label-Set Operations networks for multi-label few-shot learning - official implementation☆86Updated 8 months ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆154Updated 4 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆109Updated 6 years ago
- Pytorch implementation of Virtual Adversarial Training☆133Updated 5 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆125Updated 5 years ago
- A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation☆44Updated 7 years ago
- Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)☆80Updated 5 years ago
- IJCAI 2019 : Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph☆55Updated 5 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 5 years ago
- Learning to Self-Train for Semi-Supervised Few-Shot☆93Updated last year
- Code refactoring for paper "Domain Generalization with Adversarial Feature Learning" in VLCS datasets.☆54Updated 4 years ago
- Tensorflow codes for ICML2018, Learning Semantic Representations for Unsupervised Domain Adaptation☆110Updated 6 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆90Updated 3 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆117Updated last year
- Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976☆24Updated 5 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 7 years ago
- Code release for Transferable Curriculum for Weakly-Supervised Domain Adaptation (AAAI2019)☆18Updated 5 years ago
- code for paper Decoupling "when to update" from "how to update" [https://arxiv.org/abs/1706.02613]☆21Updated 7 years ago
- This is the PyTorch-0.4.0 implementation of few-shot learning on CIFAR-100 with graph neural networks (GNN)☆86Updated 6 years ago
- implement of paper 'Probabilistic End-to-end Noise Correction for Learning with Noisy Labels'☆16Updated 5 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆77Updated 4 years ago