Learning from Indirect Observations
Project description
LIO: Learning from Indirect Observations
A package for weakly supervised learning research based on PyTorch
Installation
pip install lio
or
git clone https://github.com/YivanZhang/lio.git
pip install -e .
Most of the modules are designed as small (higher-order) functions.
Feel free to copy-paste only what you need for your existing workflow to reduce dependencies.
References
-
Learning from Indirect Observations
Yivan Zhang, Nontawat Charoenphakdee, and Masashi Sugiyama
[arXiv] -
Learning from Aggregate Observations
Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, and Masashi Sugiyama
[arXiv] [NeurIPS'20] [poster] -
Learning Noise Transition Matrix from Only Noisy Labels
via Total Variation Regularization
Yivan Zhang, Gang Niu, and Masashi Sugiyama
[arXiv] [code] -
Approximating Instance-Dependent Noise
via Instance-Confidence Embedding
Yivan Zhang and Masashi Sugiyama
[arXiv] [code]
Project details
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