Wang, R; Pontil, M; Ciliberto, C; (2021) The Role of Global Labels in Few-Shot Classification and How to Infer Them. In: Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). (pp. pp. 27160-27170). NeurIPS

Preview

Text the_role_of_global_labels_in_f.pdf - Published Version Download (502kB) | Preview

Abstract

Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data. Recently, feature pre-training has become a ubiquitous component in state-of-the-art meta-learning methods and is shown to provide significant performance improvement. However, there is limited theoretical understanding of the connection between pre-training and meta-learning. Further, pre-training requires global labels shared across tasks, which may be unavailable in practice. In this paper, we show why exploiting pre-training is theoretically advantageous for meta-learning, and in particular the critical role of global labels. This motivates us to propose Meta Label Learning (MeLa), a novel meta-learning framework that automatically infers global labels to obtains robust few-shot models. Empirically, we demonstrate that MeLa is competitive with existing methods and provide extensive ablation experiments to highlight its key properties.

Downloads since deposit

Loading...

9Downloads

Download activity - last month

Export as XMLJSONCSV

Loading...

Download activity - last 12 months

Export as XMLCSVJSON

Loading...

Downloads by country - last 12 months

Export as JSONCSVXML

Loading...

Archive Staff Only

View Item