gorkemalgan / corrupting_labels_with_distillation
Code for paper "Label Noise Types and Their Effects on Learning"
☆16Updated 2 years ago
Alternatives and similar repositories for corrupting_labels_with_distillation:
Users that are interested in corrupting_labels_with_distillation are comparing it to the libraries listed below
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- Official Implementation of Robust Training under Label Noise by Over-parameterization☆63Updated 2 years ago
- A regularized self-labeling approach to improve the generalization and robustness of fine-tuned models☆27Updated 2 years ago
- Code for CVPR2021 paper: MOOD: Multi-level Out-of-distribution Detection☆38Updated last year
- Code for Active Mixup in 2020 CVPR☆22Updated 3 years ago
- [NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Z…☆125Updated 3 years ago
- [SafeAI'21] Feature Space Singularity for Out-of-Distribution Detection.☆80Updated 3 years ago
- [ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization☆40Updated 3 years ago
- Tiny Imagenet Visual Recognition Challenge☆36Updated 6 years ago
- An implementation of the Residual Flow algorithm for out-of-distribution detection.☆30Updated 2 years ago
- This repository contains some of the latest data augmentation techniques and optimizers for image classification using pytorch and the CI…☆29Updated 3 years ago
- [CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibra…☆32Updated 2 years ago
- This repo is for our paper: Normalization Techniques in Training DNNs: Methodology, Analysis and Application☆84Updated 3 years ago
- [CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".☆113Updated 3 years ago
- [ICLR 2022] "Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity" by Shiwei Liu,…☆27Updated 2 years ago
- Code Release for "Self-supervised Learning is More Robust to Dataset Imbalance"☆38Updated 2 years ago
- ☆31Updated 3 years ago
- [Re] Can gradient clipping mitigate label noise? (ML Reproducibility Challenge 2020)☆14Updated 4 months ago
- ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust☆13Updated 4 years ago
- ☆46Updated 4 years ago
- PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"☆71Updated 3 years ago
- Improving Calibration for Long-Tailed Recognition (CVPR2021)☆146Updated 3 years ago
- Robust Out-of-distribution Detection in Neural Networks☆72Updated 2 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆45Updated 11 months ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆135Updated 6 months ago
- Learning Loss for Active Learning Pytorch Implementation,(reproduction)☆32Updated 5 years ago
- The official PyTorch implementation - Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from t…☆77Updated 2 years ago
- [ICASSP 2020] Code release of paper 'Heterogeneous Domain Generalization via Domain Mixup'☆24Updated 4 years ago
- ☆29Updated 2 years ago
- Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)☆103Updated 3 years ago