PyTorch Adapt
Research output: Working paper › Preprint › Research
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PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at this https URL
Original language | English |
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Publisher | arXiv.org |
Number of pages | 5 |
Publication status | Published - 2022 |
Links
- https://arxiv.org/abs/2211.15673
Final published version
ID: 384619158