giorgiop / loss-correctionLinks
Robust loss functions for deep neural networks (CVPR 2017)
☆91Updated 4 years ago
Alternatives and similar repositories for loss-correction
Users that are interested in loss-correction are comparing it to the libraries listed below
Sorting:
- Gold Loss Correction☆87Updated 6 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Updated 11 months ago
- Meta-Learning based Noise-Tolerant Training☆125Updated 4 years ago
- Principled Detection of Out-of-Distribution Examples in Neural Networks☆202Updated 7 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆186Updated 6 years ago
- A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)☆175Updated 7 years ago
- Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)☆81Updated 6 years ago
- ☆129Updated 2 years ago
- Joint Optimization Framework for Learning with Noisy Labels☆45Updated 7 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 5 years ago
- ☆30Updated 7 years ago
- Official Implementation of "Random Path Selection for Incremental Learning" paper. NeurIPS 2019☆53Updated 2 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- This is a collection of Papers and Codes for Noisy Labels Problem.☆63Updated 7 years ago
- Code for the CVPR15 paper "Learning from Massive Noisy Labeled Data for Image Classification"☆119Updated 6 years ago
- Code release for Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019)☆63Updated 6 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆54Updated 6 years ago
- Code repository for the VisDA-17 experiments in our paper 'Self-ensembling for Domain Adaptation'☆74Updated 3 years ago
- Unsupervised Domain Adaptation through Self-Supervision☆79Updated 3 years ago
- Tensorflow codes for ICML2018, Learning Semantic Representations for Unsupervised Domain Adaptation☆110Updated 6 years ago
- Learning to Self-Train for Semi-Supervised Few-Shot☆94Updated 2 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆181Updated 5 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago