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
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆46Oct 29, 2022Updated 3 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆301May 22, 2023Updated 3 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
- AI Agents on DigitalOcean Gradient AI Platform • AdBuild production-ready AI agents using customizable tools or access multiple LLMs through a single endpoint. Create custom knowledge bases or connect external data.
- A curated list of resources for Learning with Noisy Labels☆2,716May 3, 2025Updated last year
- Unofficial implementation of the paper 'Adversarial Training for Free'☆23May 8, 2019Updated 7 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Jul 30, 2020Updated 5 years ago
- pytorch☆10Apr 13, 2022Updated 4 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆577Sep 14, 2020Updated 5 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Jul 5, 2019Updated 6 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆90Jun 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"☆119Jun 6, 2023Updated 3 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- 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"☆70Mar 30, 2021Updated 5 years ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Jun 11, 2024Updated 2 years ago
- Joint Optimization Framework for Learning with Noisy Labels☆45May 4, 2018Updated 8 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
- 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 7 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- [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).☆296Dec 14, 2021Updated 4 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆128Oct 24, 2023Updated 2 years ago
- NeurIPS 2020, "A Topological Filter for Learning with Label Noise".☆31Apr 11, 2025Updated last year
- ☆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.☆63Aug 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
- GPUs on demand by Runpod - Special Offer Available • AdRun AI, ML, and HPC workloads on powerful cloud GPUs—without limits or wasted spend. Deploy GPUs in under a minute and pay by the second.
- 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"☆652Nov 2, 2023Updated 2 years ago
- ☆180Jul 25, 2024Updated last year
- paper "O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks" code☆78Jul 15, 2022Updated 3 years ago
- PyTorch Code for the Paper: "Exploiting Uncertainty of Loss Landscape for Stochastic Optimization [Bhaskara et al. (2019)]☆16Apr 30, 2026Updated last month
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆38Feb 24, 2021Updated 5 years ago
- Code for paper "Label Noise Types and Their Effects on Learning"☆18Nov 14, 2022Updated 3 years ago