This is the MindSpore implementation of GLAC in the following paper.
AAAI 2023: A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
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Propose a generalized URE that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees, given unlabeled data for learning with augmented classes(LAC).
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Propose a novel risk-penalty regularization term to alleviate the issue of negative empirical risk commonly encountered by previous studies.
Our experiments are conducted on six regular-scale datasets to test the performance of our GLAC, which are Har, Msplice, Normal, Optdigits, Texture and Usps.
Framework
For more information, please check the resources below：
After installing MindSpore via the official website, you can start training and evaluation as follows:
python main.py