Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018
☆58Jun 11, 2024Updated last year
Alternatives and similar repositories for dimensionality-driven-learning
Users that are interested in dimensionality-driven-learning are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆55Nov 29, 2018Updated 7 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
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Dec 10, 2020Updated 5 years ago
- Code for paper "Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality".☆123Nov 4, 2020Updated 5 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆119Apr 25, 2017Updated 8 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.
- Code for the paper: On Symmetric Losses for Learning from Corrupted Labels☆19May 11, 2019Updated 6 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆22Jun 30, 2019Updated 6 years ago
- Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks☆327Mar 25, 2023Updated 3 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆141Jul 5, 2024Updated last year
- Learning From Noisy Singly-labeled Data☆18Feb 20, 2018Updated 8 years ago
- Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness. (MD attacks)☆11Aug 29, 2020Updated 5 years ago
- Gold Loss Correction☆88Dec 1, 2018Updated 7 years ago
- 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
- This is the official code for "Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better"☆45Aug 29, 2021Updated 4 years ago
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click and start building anything your business needs.
- Robust loss functions for deep neural networks (CVPR 2017)☆92Jun 11, 2020Updated 5 years ago
- Tensorflow source code for "CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise" (CVPR 2018)☆88Jun 26, 2018Updated 7 years ago
- Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"☆171Jun 16, 2021Updated 4 years ago
- Code repository for the robust active label correction paper.☆11Apr 12, 2018Updated 7 years ago
- Description Code for the paper "Robust Inference via Generative Classifiers for Handling Noisy Labels".☆33Sep 18, 2019Updated 6 years ago
- Code for reproducing results from our paper, Robustness of conditional GANs to noisy labels, NIPS 2018☆40Dec 23, 2018Updated 7 years ago
- Meta-Learning based Noise-Tolerant Training☆122Aug 16, 2020Updated 5 years ago
- ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust☆17Dec 14, 2020Updated 5 years ago
- AdvT-shirt-1K A Physical-world Adversarial T-shirt Dataset for Adversarial Robustness Evaluation☆14Aug 7, 2025Updated 8 months ago
- Wordpress hosting with auto-scaling on Cloudways • AdFully Managed hosting built for WordPress-powered businesses that need reliable, auto-scalable hosting. Cloudways SafeUpdates now available.
- Code for paper "Learning to Reweight Examples for Robust Deep Learning"☆268Mar 22, 2019Updated 7 years ago
- Code for the CVPR15 paper "Learning from Massive Noisy Labeled Data for Image Classification"☆119Feb 6, 2019Updated 7 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
- Robust Domain Adaptation under Noisy Environments☆18Jul 22, 2022Updated 3 years ago
- Code for Transferable Unlearnable Examples☆22Mar 11, 2023Updated 3 years ago
- Official implementation for: "Multi-Objective Interpolation Training for Robustness to Label Noise"☆41May 13, 2022Updated 3 years ago
- [NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks☆33Jul 5, 2024Updated last year
- Code and data release for the paper "Learning from noisy labels by distillation"☆21Nov 17, 2017Updated 8 years ago
- ☆13Aug 25, 2020Updated 5 years ago
- 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.
- This is a collection of Papers and Codes for Noisy Labels Problem.☆63Feb 12, 2018Updated 8 years ago
- ☆27Feb 19, 2025Updated last year
- PIPMN☆22Oct 10, 2024Updated last year
- [ICLR2023] Distilling Cognitive Backdoor Patterns within an Image☆36Oct 29, 2025Updated 5 months ago
- Code release for Transferable Curriculum for Weakly-Supervised Domain Adaptation (AAAI2019)☆18Sep 26, 2019Updated 6 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Jun 6, 2023Updated 2 years ago
- Code for the paper "Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers" published in ICLR 2019☆13Apr 25, 2019Updated 6 years ago