KaiyangZhou/Dassl.pytorch
A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.
This toolkit helps machine learning researchers develop and experiment with models that can adapt to new, unseen data environments or learn effectively with very little labeled data. It takes in datasets (potentially from multiple sources or with few labels) and provides various algorithms for building robust deep learning models. It's designed for researchers working on advanced machine learning techniques, specifically in domain adaptation, generalization, and semi-supervised learning.
1,419 stars. No commits in the last 6 months.
Use this if you are an ML researcher prototyping new deep learning methods that need to perform well on data different from what they were trained on, or when labeled data is scarce.
Not ideal if you need distributed multi-GPU training for very large-scale experiments or require extensive, detailed documentation.
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Language
Python
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MIT
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Last pushed
Nov 03, 2023
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