acmi-lab/RLSbench
Code and results accompanying our paper titled RLSbench: Domain Adaptation under Relaxed Label Shift
This project helps machine learning researchers evaluate their domain adaptation algorithms when there are changes in the distribution of labels between training and real-world data. It provides a standardized dataset setup and code to simulate various label shifts and test different algorithms, from ERM variants to self-training and test-time adaptation methods. Researchers can input their datasets and choose simulation parameters to see how their models perform under relaxed label shift conditions.
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Use this if you are an academic researcher in machine learning focused on domain adaptation and need to rigorously test algorithms under realistic label distribution shifts, particularly with limited computational resources.
Not ideal if you are a practitioner looking for a pre-built solution to apply domain adaptation to a specific business problem outside of a research context.
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35
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6
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 19, 2023
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