Fast Adaptation in Generative Models with Generative Matching Networks

Understand

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.