Low-shot learning with large-scale diffusion

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Authors Hervé Jégou, Matthijs Douze, Arthur Szlam, Bharath Hariharan
Journal/Conference Name CVPR 2018 6
Paper Category
Paper Abstract This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-shot learning, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes for which training examples are abundant. We consider a semi-supervised setting based on a large collection of images to support label propagation. This is possible by leveraging the recent advances on large-scale similarity graph construction. We show that despite its conceptual simplicity, scaling label propagation up to hundred millions of images leads to state of the art accuracy in the low-shot learning regime.
Date of publication 2017
Code Programming Language Python
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