monk1337/Awesome-Distribution-Shift
A curated list of Distribution Shift papers/articles and recent advancements.
This resource helps machine learning researchers and practitioners understand and address situations where a trained model's accuracy drops over time because the real-world data it processes changes. It provides a curated collection of research papers and articles that discuss techniques and advancements in handling these 'distribution shifts.' You'll find academic publications on how to maintain model performance and reliability when your data environment evolves.
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Use this if you are a machine learning researcher or practitioner encountering issues with model robustness and declining accuracy due to changing input data patterns, and you need to explore current research and solutions in 'distribution shift' detection and adaptation.
Not ideal if you are looking for ready-to-use software tools or code libraries to directly implement solutions without first diving into academic research.
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Oct 20, 2022
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