uber-research / learning-to-reweight-examples
Code for paper "Learning to Reweight Examples for Robust Deep Learning"
☆269Updated 6 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
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆352Updated 5 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆296Updated 6 years ago
- Gold Loss Correction☆87Updated 6 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks☆324Updated 2 years ago
- ☆175Updated 9 months ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆186Updated 6 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆286Updated 3 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- Temporal ensembling for semi-supervised learning☆158Updated 8 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- Meta-Learning based Noise-Tolerant Training☆124Updated 4 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- Virtual adversarial training with Tensorflow☆251Updated 6 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆503Updated 3 years ago
- Learning Confidence for Out-of-Distribution Detection in Neural Networks☆274Updated 7 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆181Updated 5 years ago
- Open source release of the evaluation benchmark suite described in "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆460Updated 5 years ago
- ☆129Updated 2 years ago
- Public repo for Augmented Multiscale Deep InfoMax representation learning☆400Updated 4 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning☆120Updated 9 months ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Updated 10 months ago
- Robust loss functions for deep neural networks (CVPR 2017)☆91Updated 4 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 5 years ago
- A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)☆175Updated 7 years ago
- This is a collection of Papers and Codes for Noisy Labels Problem.☆63Updated 7 years ago
- Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.☆242Updated 6 years ago
- Learning What and Where to Transfer (ICML 2019)☆248Updated 4 years ago