Physics

Pattern recognition (psychology)

Organic chemistry

Feature (linguistics)

Data mining

Engineering

Philosophy

Artificial intelligence

Chemistry

Class (philosophy)

Scale (ratio)

Shot (pellet)

Computer science

Linguistics

Feature extraction

Quantum mechanics

One shot

Artificial neural network

Mechanical engineering

Feature vector

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. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Few-Shot Classification with Feature Map Reconstruction Networks