Few-Shot Classification with Feature Map Reconstruction Networks

Davis Wertheimer; Luming Tang; Bharath Hariharan

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arXiv · 2012.01506

Few-Shot Classification with Feature Map Reconstruction Networks

Davis Wertheimer·Luming Tang·Bharath Hariharan

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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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BibTeX RIS

Davis Wertheimer, Luming Tang, Bharath Hariharan. 2020-12-02. Few-Shot Classification with Feature Map Reconstruction Networks. https://arxiv.org/abs/2012.01506

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