Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

Xueting Zhang, Debin Meng, Henry Gouk, Timothy Hospedales

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

Abstract / Description of output

Many state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple (e.g., nearest centroid) classifiers. We take an approach that is agnostic to the features used, and focus exclusively on meta-learning the final classifier layer. Specifically, we introduce MetaQDA, a Bayesian meta-learning generalisation of the classic quadratic discriminant analysis. This approach has several benefits of interest to practitioners: meta-learning is fast and memory efficient, without the need to fine-tune features. It is agnostic to the off-the-shelf features chosen, and thus will continue to benefit from future advances in feature representations. Empirically, it leads to excellent performance in cross-domain few-shot learning, class-incremental few-shot learning, and crucially for real-world applications, the Bayesian formulation leads to state-of-the-art uncertainty calibration in predictions.
Original languageEnglish
Title of host publicationProceedings of 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021
Number of pages10
ISBN (Electronic)978-1-6654-2812-5
ISBN (Print)978-1-6654-2813-2
Publication statusPublished - 28 Feb 2022
EventInternational Conference on Computer Vision 2021 - Online
Duration: 11 Oct 202117 Oct 2021

Publication series

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


ConferenceInternational Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Internet address


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