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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assumedoi:10.24963/ijcai.2021/475 fatcat:biq3yjp4gzhdvhqwxesmwz7pcu