Task: Language Identification
Input (Spanish, English): The paper explores a VAE architecture and training procedure that allows to generate new samples of a concept based on several exemplars that are shown to the model. The proposed architecture processes the set of exemplars with a recurrent neural network and aggregation procedure similar to the one used in Matching Networks. The resulting "summary" is used to condition a generative model (a VAE) that produces new samples of the same kind as the exemplars shown. The proposed aggregation and conditioning procedure are better suited to sets of exemplars that come from several classes than simple averaging. Perhaps surprisingly the model generalizes from generation conditioned on samples from 2 classes to generation conditioned on samples from 4 classes. The experiments are conducted on the OMNIGLOT dataset and are quite convincing. An explicit comparison to previous works is lacking, but this is explained in the appendices, and a comparison to architectures similar to previous work is presented.
Output (English): en