TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification

Moshe Lichtenstein, Prasanna Sattigeri, Rogerio Feris, Raja Giryes, Leonid Karlinsky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, Few-Shot Learning (FSL), or learning from very few (typically 1 or 5) examples per novel class (unseen during training), has received a lot of attention and significant performance advances. While number of techniques have been proposed for FSL, several factors have emerged as most important for FSL performance, awarding SOTA even to the simplest of techniques. These are: the backbone architecture (bigger is better), type of pre-training (meta-training vs multi-class), quantity and diversity of the base classes (the more the merrier), and using auxiliary self-supervised tasks (a proxy for increasing the diversity). In this paper we propose TAFSSL, a simple technique for improving the few shot performance in cases when some additional unlabeled data accompanies the few-shot task. TAFSSL is built upon the intuition of reducing the feature and sampling noise inherent to few-shot tasks comprised of novel classes unseen during pre-training. Specifically, we show that on the challenging miniImageNet and tieredImageNet benchmarks, TAFSSL can improve the current state-of-the-art in both transductive and semi-supervised FSL settings by more than 5 %, while increasing the benefit of using unlabeled data in FSL to above 10 % performance gain.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer Science and Business Media Deutschland GmbH
Pages522-539
Number of pages18
ISBN (Print)9783030585709
DOIs
StatePublished - 2020
Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12352 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom
CityGlasgow
Period23/08/2028/08/20

Keywords

  • Few-Shot Learning
  • Semi-supervised
  • Transductive

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