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Despite recent advances, the remaining bottlenecks in deep generative modelsare necessity of extensive training and difficulties with generalization fromsmall number of training examples. We develop a new generative model calledGenerative Matching Network which is inspired by the recently proposed matchingnetworks for one-shot learning in discriminative tasks. By conditioning on theadditional input dataset, our model can instantly learn new concepts that werenot available in the training data but conform to a similar generative process.The proposed framework does not explicitly restrict diversity of theconditioning data and also does not require an extensive inference procedurefor training or adaptation. Our experiments on the Omniglot dataset demonstratethat Generative Matching Networks significantly improve predictive performanceon the fly as more additional data is available and outperform existing stateof the art conditional generative models.

Fast Adaptation in Generative Models with Generative Matching Networks