Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification

• URL: http://arxiv.org/abs/2003.09338v2
• Date: Mon, 20 Jul 2020 12:51:34 GMT
• Title: Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
• Authors: Nikita Dvornik, Cordelia Schmid, Julien Mairal
• Abstract summary: We propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training.
• Score: 91.67977602992657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet.

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