Learning with Inadequate and Incorrect Supervision

20 Feb 2019Chen GongHengmin ZhangJian YangDacheng Tao

Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistakenly labeled due to the various human factors. Therefore, this paper proposes a novel semi-supervised learning paradigm that can handle both label insufficiency and label inaccuracy... (read more)

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