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