A curated (most recent) list of resources for Learning with Noisy Labels
-
Updated
Oct 18, 2024
A curated (most recent) list of resources for Learning with Noisy Labels
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
A repository contains a collection of resources and papers on Imbalance Learning On Graphs
MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers
"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction
Reading list for adversarial perspective and robustness in deep reinforcement learning.
A collection of algorithms for detecting and handling label noise
👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
Breast Cancer Detection - This project tackles the crucial challenge of early breast cancer detection using machine learning techniques. Using Machine learnig algorithms, Support Vector Machine, Randon Forest.
Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.
The official implementation code of Paper "PointCVaR: Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification" in AAAI 2024 (Oral)
Implementation of the paper "Improving the Accuracy-Robustness Trade-off of Classifiers via Adaptive Smoothing".
Challenging label noise called BadLabel; Robust label-noise learning called Robust DivideMix
Final Project for CS486 - Robust Machine Learning. PyTorch Implementation of DefenseGAN using the CIFAR-10 Dataset
Curated list of open source tooling for data-centric AI on unstructured data.
AQuA: A Benchmarking Tool for Label Quality Assessment
Are machines "learning" anything? This repository explores some of the concepts from the book "Artificial Intelligence, a guide for thinking humans", by Melanie Mitchell.
[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Add a description, image, and links to the robust-machine-learning topic page so that developers can more easily learn about it.
To associate your repository with the robust-machine-learning topic, visit your repo's landing page and select "manage topics."