Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This submission explores how the Neural Style representation by Gatys et al. can be used to classify the style of paintings.
The authors train a linear classifier which uses the neural style representation (gram matrices of a certain selection of layers) to predict the style. This approach is compared to a random forest classifier on the feature activations, which yields significantly higher performances. Additionally, the authors point out that ResNet can be finetuned to yield even higher performance.

Since the Neural Style algorithm works very well at transferring style, I am surprised by the fact that it works that badly for classifying style and would take that to be the main result of the submission. However in my optinion the submission is missing any analysis of why the representation is that much worse than other methods. From examples of correct predictions and wrong predictions, one might get interesting insights in what exactly the style representation is incoding and what it is missing with respect to style classification.
review_title: Interesting work but missing analyses.

final_score: rating: 4: Ok but not good enough - rejection
review_content: This submission classifies painting style from pre-trained CNN feature spaces:
a) using a linear classifier from the ‘style features’ used for style transfer in Gatys et al. 2016
b) using a random forrest classifier from the raw feature representations of different layers of the VGG-19 network
c) fine-tuning res-net

It shows that res-net gives best top-1 accuracy followed by mid-level feature representations of the VGG.

While style classification is an interesting problem, the novelty and technical contribution of this work is limited. Style classification with fine-tuned CNNs as well as from the ‘style features’ has been done before in a more extensive manner [1,2].
In my opinion, this submission does not add any technical novelty or new insights in the nature of style classification.
Therefore I cannot recommend acceptance for presentation at the ICLR Workshops.


[1] Karayev, Sergey, et al. "Recognizing image style." arXiv preprint arXiv:1311.3715 (2013).
[2] Chu, Wei-Ta, and Yi-Ling Wu. "Deep Correlation Features for Image Style Classification." Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016.

review_title: Interesting work but limited novelty and new insights

Decision: Reject