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Poster
Universal Semi-Supervised Learning
Zhuo Huang · Chao Xue · Bo Han · Jian Yang · Chen Gong

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @
in Poster Session 3 »

Universal Semi-Supervised Learning (UniSSL) aims to solve the open-set problem where both the class distribution (i.e., class set) and feature distribution (i.e., feature domain) are different between labeled dataset and unlabeled dataset. Such a problem seriously hinders the realistic landing of classical SSL. Different from the existing SSL methods targeting at the open-set problem that only study one certain scenario of class distribution mismatch and ignore the feature distribution mismatch, we consider a more general case where a mismatch exists in both class and feature distribution. In this case, we propose a ''Class-shAring data detection and Feature Adaptation'' (CAFA) framework which requires no prior knowledge of the class relationship between the labeled dataset and unlabeled dataset. Particularly, CAFA utilizes a novel scoring strategy to detect the data in the shared class set. Then, it conducts domain adaptation to fully exploit the value of the detected class-sharing data for better semi-supervised consistency training. Exhaustive experiments on several benchmark datasets show the effectiveness of our method in tackling open-set problems.

Keywords: Domain Adaptation · Semi-Supervised Learning

Author Information

Zhuo Huang (Nanjing University of Science and Technology)
Chao Xue (JD Explore Academy)
Bo Han (HKBU / RIKEN)
Jian Yang (Nanjing University of Science and Technology)
Chen Gong (Nanjing University of Science and Technology)

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