Code for paper "Learning to Reweight Examples for Robust Deep Learning"
☆268Mar 22, 2019Updated 7 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. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆355May 18, 2019Updated 6 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆295Dec 14, 2021Updated 4 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆55Nov 29, 2018Updated 7 years ago
- NeurIPS 2019 - Learning Data Manipulation for Augmentation and Weighting☆110Sep 5, 2020Updated 5 years ago
- Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks☆327Mar 25, 2023Updated 3 years ago
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Dec 10, 2020Updated 5 years ago
- Gold Loss Correction☆88Dec 1, 2018Updated 7 years ago
- Meta-Learning based Noise-Tolerant Training☆122Aug 16, 2020Updated 5 years ago
- PyTorch code for BMVC 2018 paper: "Self-Paced Learning with Adaptive Visual Embeddings"☆21Jun 26, 2019Updated 6 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆520Aug 19, 2021Updated 4 years ago
- ☆180Jul 25, 2024Updated last year
- An implementation of the paper "Learning to Reweight Examples for Robust Deep Learning" from ICML 2018 with PyTorch and Higher.☆28Oct 11, 2022Updated 3 years ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Jun 11, 2024Updated last year
- A curated list of resources for Learning with Noisy Labels☆2,717May 3, 2025Updated last year
- GPU virtual machines on DigitalOcean Gradient AI • AdGet to production fast with high-performance AMD and NVIDIA GPUs you can spin up in seconds. The definition of operational simplicity.
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆224Jul 30, 2020Updated 5 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆119Apr 25, 2017Updated 9 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆22Jun 30, 2019Updated 6 years ago
- [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss☆702Dec 25, 2021Updated 4 years ago
- higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual tr…☆1,627Mar 25, 2022Updated 4 years ago
- Code for Paper "Incremental Few-Shot Learning with Attention Attractor Networks"☆123Mar 19, 2020Updated 6 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆99Mar 1, 2022Updated 4 years ago
- Code repository for the robust active label correction paper.☆11Apr 12, 2018Updated 8 years ago
- ☆807Dec 29, 2020Updated 5 years ago
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust☆17Dec 14, 2020Updated 5 years ago
- Reproduction of "Model-Agnostic Meta-Learning" (MAML) and "Reptile".☆189Mar 21, 2019Updated 7 years ago
- A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).☆141Oct 30, 2020Updated 5 years ago
- A Closer Look at Accuracy vs. Robustness☆87May 17, 2021Updated 4 years ago
- Pytorch implementation for "Large-Scale Long-Tailed Recognition in an Open World" (CVPR 2019 ORAL)☆870Jul 16, 2022Updated 3 years ago
- Unsupervised Feature Learning via Non-parametric Instance Discrimination☆759Mar 25, 2021Updated 5 years ago
- code for paper Decoupling "when to update" from "how to update" [https://arxiv.org/abs/1706.02613]☆22Nov 16, 2017Updated 8 years ago
- Self-Supervised Learning for OOD Detection (NeurIPS 2019)☆269Apr 29, 2021Updated 5 years ago
- [CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".☆113Jan 9, 2022Updated 4 years 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.
- Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search☆89May 29, 2019Updated 6 years ago
- ICME2022 Special Session “Beyond Accuracy: Responsible, Responsive, and Robust Multimedia Retrieval ”☆12Jun 3, 2024Updated last year
- Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"☆2,725Jan 19, 2020Updated 6 years ago
- Improving Generalization via Scalable Neighborhood Component Analysis☆137Jun 12, 2023Updated 2 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆577Sep 14, 2020Updated 5 years ago
- [NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation …☆572Jan 6, 2026Updated 4 months ago
- A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch☆2,057Jul 17, 2023Updated 2 years ago