Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings

We consider a novel formulation of the problem of Active Few-Shot Classification (AFSC) where the objective is to classify a small, initially unlabeled, dataset given a very restrained labeling budget. This problem can be seen as a rival paradigm to classical Transductive Few-Shot Classification (TF...

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Veröffentlicht in:	arXiv.org 2022-09
Hauptverfasser:	Aymane Abdali, Gripon, Vincent, Lucas Drumetz, Boguslawski, Bartosz
Format:	Artikel
Sprache:	eng
Schlagworte:	Budgets Classification Labeling Learning Statistical inference
Online-Zugang:	Volltext
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