Awesome Learning with Label Noise
A curated collection of academic papers, code, and resources for learning with noisy labels in machine learning.
What is Awesome Learning with Label Noise?
Awesome Learning with Noisy Labels is a curated GitHub repository that serves as a centralized resource hub for research on training machine learning models with incorrect or unreliable data labels. It addresses the fundamental problem where real-world datasets often contain labeling errors, which can severely degrade model performance. The collection helps practitioners find state-of-the-art methods to make their models robust to such noise.
Machine learning researchers, data scientists, and graduate students working on robust learning, data quality, or any application where label reliability is a concern (e.g., computer vision, medical imaging, crowdsourced data).
It saves significant literature review time by aggregating decades of scattered research into one searchable list, often with direct links to code. Unlike generic paper lists, it focuses specifically on label noise and emphasizes practical implementation through code availability.
Overview
A curated list of resources for Learning with Noisy Labels
Use Cases
Best For
- Researchers conducting literature reviews on robust learning or label noise methods
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