monk1337/Awesome-Robust-Machine-Learning
A curated list of Robust Machine Learning papers/articles and recent advancements.
Building reliable machine learning models that perform consistently, even with noisy or unexpected data, is crucial. This resource collects research papers, code, datasets, and tutorials focused on making AI models more stable and trustworthy. It's for machine learning researchers and practitioners who want to develop or evaluate models that maintain performance under varying conditions or when exposed to adversarial inputs.
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Use this if you are a machine learning researcher or engineer looking for resources to build, test, or understand robust machine learning models that are less susceptible to data variations or adversarial attacks.
Not ideal if you are a business user seeking a quick, off-the-shelf solution for an immediate problem without delving into the underlying academic research or technical implementations.
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Oct 13, 2022
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