mfederici/dsit
Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
This project provides an implementation of various models and datasets designed to tackle challenges related to 'distribution shifts' in machine learning. It helps researchers and machine learning practitioners evaluate how well their models generalize when the data used for training differs from the data encountered during deployment. You feed in custom datasets, or use provided variants of CMNIST, and it outputs performance metrics showing robustness to these shifts.
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Use this if you are a machine learning researcher or practitioner interested in understanding and mitigating distribution shifts, and you need a framework to experiment with models like VIB, DANN, IRM, VREx, or CDANN.
Not ideal if you are looking for an out-of-the-box solution to apply to a specific business problem without deep engagement in machine learning research or model customization.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Nov 02, 2021
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