Searching for Robustness: Loss Learning for Noisy Classification Tasks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We present a “learning to learn” approach for discovering white-box classification loss functions that are robust to label noise in the training data. We parameterise a flexible family of loss functions using Taylor polynomials, and apply evolutionary strategies to search for noise-robust losses in this space. To learn re-usable loss functions that can apply to new tasks, our fitness function scores their performance in aggregate across a range of training datasets and architectures. The resulting white-box loss provides a simple and fast “plug-and-play” module that enables effective labelnoise-robust learning in diverse downstream tasks, without requiring a special training procedure or network architecture. The efficacy of our loss is demonstrated on a variety of datasets with both synthetic and real label noise, where we compare favourably to prior work.
Original languageEnglish
Title of host publicationProceedings of 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021
PublisherIEEE
Pages6650-6659
Number of pages10
ISBN (Electronic)978-1-6654-2812-5
ISBN (Print)978-1-6654-2813-2
DOIs
Publication statusPublished - 28 Feb 2022
EventInternational Conference on Computer Vision 2021 - Online
Duration: 11 Oct 202117 Oct 2021
https://iccv2021.thecvf.com/home

Publication series

Name2021 IEEE/CVF International Conference on Computer Vision (ICCV)
PublisherIEEE
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

ConferenceInternational Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Period11/10/2117/10/21
Internet address

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