reiinakano/invariant-risk-minimization
Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
This project helps machine learning researchers understand and reproduce the 'Invariant Risk Minimization' technique, specifically for image classification tasks. It takes image datasets with different 'environments' (e.g., Colored MNIST) and outputs a model that ideally performs well even on new, unseen environments. This is for researchers and practitioners in machine learning focused on building robust and generalizable AI models.
No commits in the last 6 months.
Use this if you are a machine learning researcher or practitioner investigating how to build models that are less sensitive to shifts in data distributions between training and deployment environments.
Not ideal if you are looking for a plug-and-play solution for a real-world, production-ready system without deep understanding of the underlying research.
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Jupyter Notebook
License
MIT
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Last pushed
Feb 13, 2020
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