Few-Shot Learning with Graph Neural Networks

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Authors Victor Garcia, Joan Bruna
Journal/Conference Name 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings
Paper Category
Paper Abstract We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on 'relational' tasks.
Date of publication 2017
Code Programming Language Multiple
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