Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The paper proposes two primary novel ideas of doing fast style transfer even on styles which are as yet unseen by the model. First, the authors extend the model of Johnson et al by making it take a conditional vector which encodes the style to be transferred. Second, they propose another neural network architecture which generates such a conditional style vector. This allows the authors directly plug the conditional vector generator (which they call Style Condition Network) into the Conditional Fast Style Transfer Network, allowing them to train the entire architecture end-to-end. In addition the architecture is flexible enough that one can represent the styles as a continuous vector and hence can potentially represent a mixture of different styles. The experimental results show that the model has been successfully able to transfer the styles. 

While the approach proposed seems to be novel, I thought the experimental section is a bit weak. The styles on which the model has been trained on seems to have transferred well. However those on which the model is not trained, the transfer seems to be a bit weak. Furthermore, since all the results are qualitative, it is somewhat hard to get a sense how good or bad the proposed approach is, with regards to the unseen styles. 

In any case I think paper has enough interesting ideas that it justifies being spoken about at a workshop. 


review_title: Interesting ideas for doing fast style transfer using unseen styles

final_score: rating: 6: Marginally above acceptance threshold
review_content: - A brief summary of the paper's contributions, in the context of prior work.

This paper addresses the problem of fast style transfer for unseen style images.  The paper builds on the fast conv-deconv network of Johnson et al.  The key technical insight is to have a “conditional vector” input which encodes different styles.  The vector is concatenated to the convolutional responses of the content image.  This representation is then deconvolved to the final style-transferred image output.  The network can also be extended to regress to the conditional vector given a style image.

- An assessment of novelty, clarity, significance, and quality.

The approach is novel, as far as I’m aware, but I’m not an expert in this topic.  The paper is clear enough and cites relevant work that I’m familiar with in this space.  As I’m not an expert in this area, I’m happy to support a champion. 

- A list of pros and cons (reasons to accept/reject).

Pros: The approach appears to be novel. 

Cons: The paper shows qualitative results only and doesn’t compare against any baselines.  However, given the limited number of pages for the paper submission, this is probably ok.
review_title: feedforward network for arbitrary styles

Decision: Accept