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
Alternatives and similar repositories for Truncated-Loss
Users that are interested in Truncated-Loss are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- 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
- Meta-Learning based Noise-Tolerant Training☆123Aug 16, 2020Updated 5 years ago
- [Re] Can gradient clipping mitigate label noise? (ML Reproducibility Challenge 2020)☆14Sep 3, 2024Updated last year
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆171Jun 16, 2021Updated 4 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆46Oct 29, 2022Updated 3 years ago
- Open source password manager - Proton Pass • AdSecurely store, share, and autofill your credentials with Proton Pass, the end-to-end encrypted password manager trusted by millions.
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆298May 22, 2023Updated 2 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆520Aug 19, 2021Updated 4 years ago
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆78Jun 15, 2021Updated 4 years ago
- A curated list of resources for Learning with Noisy Labels☆2,720May 3, 2025Updated 10 months ago
- Unofficial implementation of the paper 'Adversarial Training for Free'☆23May 8, 2019Updated 6 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆225Jul 30, 2020Updated 5 years ago
- pytorch☆10Apr 13, 2022Updated 3 years ago
- 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
- End-to-end encrypted email - Proton Mail • AdSpecial offer: 40% Off Yearly / 80% Off First Month. All Proton services are open source and independently audited for security.
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆89Jun 30, 2019Updated 6 years ago
- Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters☆30Nov 21, 2020Updated 5 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆120Jun 6, 2023Updated 2 years ago
- Learning with Noisy Labels by adopting a peer prediction loss function.☆35Mar 3, 2020Updated 6 years ago
- PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"☆71Mar 30, 2021Updated 4 years ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Jun 11, 2024Updated last year
- Joint Optimization Framework for Learning with Noisy Labels☆45May 4, 2018Updated 7 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆141Jul 5, 2024Updated last year
- A Survey☆572Feb 13, 2023Updated 3 years ago
- Virtual machines for every use case on DigitalOcean • AdGet dependable uptime with 99.99% SLA, simple security tools, and predictable monthly pricing with DigitalOcean's virtual machines, called Droplets.
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Dec 10, 2020Updated 5 years ago
- noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.☆59Aug 7, 2022Updated 3 years ago
- Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks☆327Mar 25, 2023Updated 3 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆355May 18, 2019Updated 6 years ago
- [ICCV'19] Improving Adversarial Robustness via Guided Complement Entropy☆39Aug 2, 2019Updated 6 years ago
- This is a collection of Papers and Codes for Noisy Labels Problem.☆63Feb 12, 2018Updated 8 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
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆127Oct 24, 2023Updated 2 years ago
- NeurIPS 2020, "A Topological Filter for Learning with Label Noise".☆30Apr 11, 2025Updated 11 months ago
- Bare Metal GPUs on DigitalOcean Gradient AI • AdPurpose-built for serious AI teams training foundational models, running large-scale inference, and pushing the boundaries of what's possible.
- ☆17Nov 27, 2023Updated 2 years ago
- PyTorch code for BMVC 2018 paper: "Self-Paced Learning with Adaptive Visual Embeddings"☆21Jun 26, 2019Updated 6 years ago
- A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.☆62Aug 28, 2021Updated 4 years ago
- This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.☆236Sep 20, 2021Updated 4 years ago
- ICLR 2022 (Spolight): Continual Learning With Filter Atom Swapping☆16Jul 5, 2023Updated 2 years ago
- Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"☆653Nov 2, 2023Updated 2 years ago
- ☆179Jul 25, 2024Updated last year