danieltan07 / learning-to-reweight-examples
PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
☆352Updated 5 years ago
Alternatives and similar repositories for learning-to-reweight-examples
Users that are interested in learning-to-reweight-examples are comparing it to the libraries listed below
Sorting:
- Code for paper "Learning to Reweight Examples for Robust Deep Learning"☆269Updated 6 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆286Updated 3 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆296Updated 6 years ago
- Meta-Learning based Noise-Tolerant Training☆124Updated 4 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].☆174Updated 3 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 5 years ago
- Learning What and Where to Transfer (ICML 2019)☆248Updated 4 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆186Updated 6 years ago
- Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.☆242Updated 6 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- Code for reproducing Manifold Mixup results (ICML 2019)☆493Updated last year
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆504Updated 3 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
- Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning☆120Updated 9 months ago
- ☆154Updated 5 years ago
- ☆175Updated 9 months ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 5 years ago
- Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"☆256Updated 7 years ago
- Learning deep representations by mutual information estimation and maximization☆324Updated 6 years ago
- This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.☆233Updated 3 years ago
- Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning☆249Updated 7 years ago
- Tensorflow implementation of "Learning to Balance: Bayesian Meta-learning for Imbalanced and Out-of-distribution Tasks" (ICLR 2020 oral)☆101Updated 4 years ago
- ☆129Updated 2 years ago
- Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks☆324Updated 2 years ago
- Meta Learning for Semi-Supervised Few-Shot Classification☆560Updated 5 years ago
- Implementation of the mixup training method☆466Updated 6 years ago
- Gold Loss Correction☆87Updated 6 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago