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EULAC

Description: The package includes the Python 3.7 implementation of the Eulac approach [1], which aims to identify the augmented classes (classes unobserved during training but emerging in testing) by exploiting unlabeled data. The package includes two implementations of the Eulac approach, where Eulac-RKHS trains classifiers with the kernel-based hypothesis space and Eulac-DNN is based on deep models.

Reference:
[1] Yu-Jie Zhang, Peng Zhao, Lanjihong Ma, and Zhi-Hua Zhou. An unbiased risk estimator for learning with augmented classes. In: Advances in Neural Information Processing Systems 33 (NeurIPS'20), 2020.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou.

ATTN2: This package was developed by Yu-Jie Zhang, Lanjihong Ma and Yong Bai. For any problem concerning the code, please feel free to contact Mr. Zhang.

Requirement: This package is developed with Python 3.7 and the dependencies are specified in requirements.txt.

Download: [code] (87KB) [dataset] (270MB)
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