eprintid: 10151383
rev_number: 13
eprint_status: archive
userid: 699
dir: disk0/10/15/13/83
datestamp: 2022-07-07 15:25:05
lastmod: 2022-12-05 15:34:09
status_changed: 2022-07-07 15:25:05
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Wang, R
creators_name: Pontil, M
creators_name: Ciliberto, C
title: The Role of Global Labels in Few-Shot Classification and How to Infer Them
ispublished: pub
divisions: C05
divisions: F48
divisions: B04
divisions: UCL
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2021
date_type: published
publisher: NeurIPS
official_url: https://proceedings.neurips.cc/paper/2021/file/e3b6fb0fd4df098162eede3313c54a8d-Paper.pdf
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1882163
lyricists_name: Pontil, Massimiliano
lyricists_id: MPONT27
actors_name: Jenkins, Alexander
actors_name: Flynn, Bernadette
actors_id: AJENK21
actors_id: BFFLY94
actors_role: owner
actors_role: impersonator
full_text_status: public
publication: Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
volume: 32
pagerange: 27160-27170
book_title: Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
citation:        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       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10151383/1/the_role_of_global_labels_in_f.pdf