Feature Redirection Network for Few-Shot Classification

Yanan Wang, Guoqiang Zhong*, Yuxu Mao, Kaizhu Huang

*Corresponding author for this work

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

2 Citations (Scopus)


Few-shot classification aims to learn novel categories by giving few labeled samples. How to make best use of the limited data to obtain a learner with fast learning ability has become a challenging problem. In this paper, we propose a feature redirection network (FRNet) for few-shot classification to make the features more discriminative. The proposed FRNet not only highlights relevant category features of support samples, but also learns how to generate task-relevant features of query samples. Experiments conducted on three datasets have demonstrate its superiority over the state-of-the-art methods.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030638191
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference27th International Conference on Neural Information Processing, ICONIP 2020


  • Feature redirection
  • Few-shot classification
  • Task-relevant


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