Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The paper reports on a conditional VAE that generates samples similar to few samples it is conditioned upon.
The conditioning samples in this work are taken from few different classes. It is shown empirically that a vector summary of the conditioning dataset that simply averages representations of the individual samples doesn't encode the information well. Instead the authors propose to aggregate that information similarly to the method used in matching networks. This method seems to be working better.
Overall it's an interesting idea. The model description could be clearer however.
review_title: Interesting work

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper proposes a interesting conditional generative model by using generative matching networks so that the model is able to carry out one-shot or few-shot learning.
However, the notations are very confusing and lack of clarity.
Figure 1 shows a neural structure but there seems no corresponding text explaining it.
The authors introduce the model by telling the story from basic version, but I couldn’t follow the further modification.
Does it mean the basic version uses standard normal to be the prior and further the model employs an inference network?
I can understand the authors attempt to train a conditional generative distribution to produce data with an intermediate latent variable. 
But in order to argue that the latent variable z brings the stochasticity and generalization ability, the current experiments are not sufficient enough and lack of baseline models. 
Even the comparison with VAE only shows marginal improvement.
I think this paper needs a bit more work on the design of the paper presentations and experiment. 
review_title: interesting but needs further work to improve

Decision: Accept