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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.
|Title of host publication||International Conference on Computer Vision (ICCV 2021)|
|Number of pages||12|
|Publication status||Accepted/In press - 22 Jul 2021|
|Event||International Conference on Computer Vision 2021 - Online|
Duration: 11 Oct 2021 → 17 Oct 2021
|Conference||International Conference on Computer Vision 2021|
|Abbreviated title||ICCV 2021|
|Period||11/10/21 → 17/10/21|
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