MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations

Calum Heggan, Tim Hospedales, Sam Budgett, Mehrdad Yaghoobi

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

Abstract / Description of output

Contrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets. A key reason that these powerful features enable data-efficient learning of downstream tasks is that they provide augmentation invariance, which is often a useful inductive bias. However, the amount and type of invariances preferred is not known apriori, and varies across different downstream tasks. We therefore propose a multi-task self-supervised framework (MT-SLVR) that learns both variant and invariant features in a parameter-efficient manner. Our multi-task representation provides a strong and flexible feature that benefits diverse downstream tasks. We evaluate our approach on few-shot classification tasks drawn from a variety of audio domains and demonstrate improved classification performance on all of them
Original languageEnglish
Title of host publicationProc. INTERSPEECH 2023
PublisherInternational Speech Communication Association
Number of pages5
Publication statusPublished - 20 Aug 2023
EventInterspeech 2023 - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023
Conference number: 24

Publication series

ISSN (Electronic)1990-9772


ConferenceInterspeech 2023
Internet address


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