Demystifying Assumptions in Learning to Discover Novel Classes

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

In learning to discover novel classes (L2DNC), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However, the rigorous definition of L2DNC is unexplored, which results in that its implicit assumptions are still unclear. In this paper, we demystify assumptions behind L2DNC and find that high-level semantic features should be shared among the seen and unseen classes. This naturally motivates us to link L2DNC to meta-learning that has exactly the same assumption as L2DNC. Based on this finding, L2DNC is not only theoretically solvable, but can also be empirically solved by meta-learning algorithms after slight modifications. This L2DNC methodology significantly reduces the amount of unlabeled data needed for training and makes it more practical, as demonstrated in experiments. The use of very limited data is also justified by the application scenario of L2DNC: since it is unnatural to label only seen-class data, L2DNC is sampling instead of labeling in causality. Therefore, unseen-class data should be collected on the way of collecting seen-class data, which is why they are novel and first need to be clustered.

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