YujiaBao/tofu
"Learning Stable Classifiers by Transferring Unstable Features" ICML 2022
This project helps machine learning researchers or practitioners develop more reliable classification models by leveraging characteristics from one related domain to improve predictions in another. It takes existing labeled datasets from a 'source' domain and applies its 'unstable features' to enhance a classifier's stability and accuracy on a 'target' domain, resulting in more robust models even when the target data is challenging. This is for researchers and practitioners working on real-world classification tasks.
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Use this if you need to build a stable classifier for a specific domain, but you have limited or challenging data and believe a related domain's data could offer useful insights, even if those insights are subtle or 'unstable' in their original context.
Not ideal if you are looking for a general-purpose, off-the-shelf classifier and do not have access to or expertise in leveraging related domain datasets for transfer learning.
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14
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4
Language
Python
License
MIT
Category
Last pushed
Jul 24, 2022
Commits (30d)
0
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