Few-Shot Learning with Graph Neural Networks

ICLR 2018 Victor Garcia SatorrasJoan Bruna Estrach

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... (read more)

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