Few-Shot Classification with Feature Map Reconstruction Networks

• URL: http://arxiv.org/abs/2012.01506v2
• Date: Tue, 27 Apr 2021 17:54:22 GMT
• Title: Few-Shot Classification with Feature Map Reconstruction Networks
• Abstract summary: We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches.
• Score: 41.50028437830414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.

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