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Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
[article]
2021
arXiv
pre-print
Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. Prototypical networks incorporate few-shot metric learning, by constructing a class prototype in the form of a mean vector of the embedded support points within a class. The performance of prototypical networks in extreme few-shot scenarios (like one-shot) degrades drastically, mainly due to the desuetude of variations within the clusters while constructing prototypes. In this
arXiv:2102.08074v1
fatcat:zg5tzjuvgvbxnfdp2tao4k2ocy