Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112
☆191Dec 27, 2020Updated 5 years ago
Alternatives and similar repositories for SCELoss-Reproduce
Users that are interested in SCELoss-Reproduce are comparing it to the libraries listed below
Sorting:
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆171Jun 16, 2021Updated 4 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆129Nov 12, 2019Updated 6 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆141Jul 5, 2024Updated last year
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆576Sep 14, 2020Updated 5 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆140Jul 5, 2019Updated 6 years ago
- A curated list of resources for Learning with Noisy Labels☆2,720May 3, 2025Updated 10 months ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Jun 11, 2024Updated last year
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆39Aug 26, 2018Updated 7 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆520Aug 19, 2021Updated 4 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆298May 22, 2023Updated 2 years ago
- [Machine Learning 2023] Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness☆17Jul 5, 2024Updated last year
- Code for the paper: On Symmetric Losses for Learning from Corrupted Labels☆19May 11, 2019Updated 6 years ago
- [NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks☆33Jul 5, 2024Updated last year
- Dataset accompanying the paper "Adaptive Methods for Real-World Domain Generalization"☆16Aug 17, 2023Updated 2 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆225Jul 30, 2020Updated 5 years ago
- Robust loss functions for deep neural networks (CVPR 2017)☆92Jun 11, 2020Updated 5 years ago
- [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training☆130Oct 17, 2021Updated 4 years ago
- Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness. (MD attacks)☆11Aug 29, 2020Updated 5 years ago
- (ICCV'19 Best Paper Nomination) Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation☆187Oct 24, 2019Updated 6 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆355May 18, 2019Updated 6 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆22Jun 30, 2019Updated 6 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆120Jun 6, 2023Updated 2 years ago
- IJCAI2020 & IJCV2021 Unsupervised Scene Adaptation with Memory Regularization in vivo☆396Oct 24, 2025Updated 4 months ago
- convolutional code for feed-forward noise model☆16Nov 26, 2014Updated 11 years ago
- Gold Loss Correction☆88Dec 1, 2018Updated 7 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆89Jun 30, 2019Updated 6 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆293Dec 14, 2021Updated 4 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Dec 10, 2020Updated 5 years ago
- Unofficial PyTorch implementation of "Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Ne…☆22Dec 19, 2019Updated 6 years ago
- The official project for CVPR19 paper: Domain-Symmetric Networks for Adversarial Domain Adaptation☆84Mar 29, 2021Updated 4 years ago
- PyTorch implementation of the "Learning an Adaptive Learning Rate Schedule" paper found here: https://arxiv.org/abs/1909.09712.☆12Jan 15, 2020Updated 6 years ago
- [CVPRW'22] A privacy attack that exploits Adversarial Training models to compromise the privacy of Federated Learning systems.☆12Jul 7, 2022Updated 3 years ago
- NN 2023☆23Nov 9, 2022Updated 3 years ago
- Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters☆30Nov 21, 2020Updated 5 years ago
- This repository contains the annotations used for evaluating Unsupervised Domain Adaptation on EPIC Kitchens, with individual kitchens us…☆13Jun 2, 2020Updated 5 years ago
- [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss☆698Dec 25, 2021Updated 4 years ago
- This is the repository for the AI2019, tutorial on adversarial machine learning☆17Jul 20, 2020Updated 5 years ago
- Meta-Learning based Noise-Tolerant Training☆123Aug 16, 2020Updated 5 years ago
- label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful☆2,262Oct 17, 2024Updated last year