A Learned Representation For Artistic Style
The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style.
CONTRIBUTIONS The authors propose a simple architectural modification (conditional instance normalization) for the task of feedforward neural style transfer that allows a single network to apply many different styles to input images. Experiments show that the proposed multi-style networks produce qualitatively similar images as single-style networks, train as fast as single-style networks, and achieve comparable losses as single-style networks. In addition, the authors shows that new styles can be incrementally added to multi-style networks with minimal finetuning, and that convex combinations of per-style parameters can be used for feedforward style blending. The authors have released open-source code and pretrained models allowing others to replicate the experimental results. NOVELTY The problem setup is very similar to prior work on feedforward neural style transfer, but the paper is the first to my knowledge that uses a single network to apply different styles to input images; the proposed conditional instance normalization layer is also novel. This paper is also the first that demonstrates feedforward neural style blending; though not described in published literature, optimization-based neural style blending had previously been demonstrated in https://github.com/jcjohnson/neural-style. MISSING CITATION The following paper was concurrent with Ulyanov et al (2016a) and Johnson et al in demonstrating feedforward neural style transfer, though it did not use the Gram-based formulation of Gatys et al: Li and Wand, *Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks*, ECCV 2016 CLARITY The paper is very well written and easy to follow. SIGNIFICANCE Though simple, the proposed method is a significant addition to the growing field of neural style transfer. Its benefits are especially clear for mobile applications, which are often constrained in both disk space and bandwidth. Using the proposed method, only a single trained network needs to be transmitted and stored on the mobile device; in addition the ability of the proposed method to incrementally learn new styles means that new styles can be added by transmitting only a small number of new style-specific parameters to a mobile device. EVALUATION Like many other papers on neural style transfer, the results are mostly qualitative. Following existing literature, the authors use style and content loss as a quantitative measure of quality, but these metrics are unfortunately not always well-correlated with the perceptual quality of the results. I find the results of this paper convincing, but I wish that this and other papers on this topic could find a way to evaluate their results more quantitatively. SUMMARY The problem and method are slightly incremental, but the several improvements over prior work make this paper a significant addition to the growing literature on neural style transfer. The paper is well-written and its experiments convincingly validate the benefits of the method. Overall I believe the paper would be a valuable addition to the conference. Pros - Simple modification to feedforward neural style transfer with several improvements over prior work - Strong qualitative results - Well-written - Open-source code has already been released Cons - Slightly incremental - Somewhat lacking in quantitative evaluation, but not any more so than prior work on this topic
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A Learned Representation For Artistic Style
The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



CONTRIBUTIONS The authors propose a simple architectural modification (conditional instance normalization) for the task of feedforward neural style transfer that allows a single network to apply many different styles to input images. Experiments show that the proposed multi-style networks produce qualitatively similar images as single-style networks, train as fast as single-style networks, and achieve comparable losses as single-style networks. In addition, the authors shows that new styles can be incrementally added to multi-style networks with minimal finetuning, and that convex combinations of per-style parameters can be used for feedforward style blending. The authors have released open-source code and pretrained models allowing others to replicate the experimental results. NOVELTY The problem setup is very similar to prior work on feedforward neural style transfer, but the paper is the first to my knowledge that uses a single network to apply different styles to input images; the proposed conditional instance normalization layer is also novel. This paper is also the first that demonstrates feedforward neural style blending; though not described in published literature, optimization-based neural style blending had previously been demonstrated in https://github.com/jcjohnson/neural-style. MISSING CITATION The following paper was concurrent with Ulyanov et al (2016a) and Johnson et al in demonstrating feedforward neural style transfer, though it did not use the Gram-based formulation of Gatys et al: Li and Wand, *Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks*, ECCV 2016 CLARITY The paper is very well written and easy to follow. SIGNIFICANCE Though simple, the proposed method is a significant addition to the growing field of neural style transfer. Its benefits are especially clear for mobile applications, which are often constrained in both disk space and bandwidth. Using the proposed method, only a single trained network needs to be transmitted and stored on the mobile device; in addition the ability of the proposed method to incrementally learn new styles means that new styles can be added by transmitting only a small number of new style-specific parameters to a mobile device. EVALUATION Like many other papers on neural style transfer, the results are mostly qualitative. Following existing literature, the authors use style and content loss as a quantitative measure of quality, but these metrics are unfortunately not always well-correlated with the perceptual quality of the results. I find the results of this paper convincing, but I wish that this and other papers on this topic could find a way to evaluate their results more quantitatively. SUMMARY The problem and method are slightly incremental, but the several improvements over prior work make this paper a significant addition to the growing literature on neural style transfer. The paper is well-written and its experiments convincingly validate the benefits of the method. Overall I believe the paper would be a valuable addition to the conference. Pros - Simple modification to feedforward neural style transfer with several improvements over prior work - Strong qualitative results - Well-written - Open-source code has already been released Cons - Slightly incremental - Somewhat lacking in quantitative evaluation, but not any more so than prior work on this topic
assistant
CONTRIBUTIONS The authors propose a simple architectural modification (conditional instance normalization) for the task of feedforward neural style transfer that allows a single network to apply many different styles to input images. Experiments show that the proposed multi-style networks produce qualitatively similar images as single-style networks, train as fast as single-style networks, and achieve comparable losses as single-style networks. In addition, the authors shows that new styles can be incrementally added to multi-style networks with minimal finetuning, and that convex combinations of per-style parameters can be used for feedforward style blending. The authors have released open-source code and pretrained models allowing others to replicate the experimental results. NOVELTY The problem setup is very similar to prior work on feedforward neural style transfer, but the paper is the first to my knowledge that uses a single network to apply different styles to input images; the proposed conditional instance normalization layer is also novel. This paper is also the first that demonstrates feedforward neural style blending; though not described in published literature, optimization-based neural style blending had previously been demonstrated in https://github.com/jcjohnson/neural-style. MISSING CITATION The following paper was concurrent with Ulyanov et al (2016a) and Johnson et al in demonstrating feedforward neural style transfer, though it did not use the Gram-based formulation of Gatys et al: Li and Wand, *Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks*, ECCV 2016 CLARITY The paper is very well written and easy to follow. SIGNIFICANCE Though simple, the proposed method is a significant addition to the growing field of neural style transfer. Its benefits are especially clear for mobile applications, which are often constrained in both disk space and bandwidth. Using the proposed method, only a single trained network needs to be transmitted and stored on the mobile device; in addition the ability of the proposed method to incrementally learn new styles means that new styles can be added by transmitting only a small number of new style-specific parameters to a mobile device. EVALUATION Like many other papers on neural style transfer, the results are mostly qualitative. Following existing literature, the authors use style and content loss as a quantitative measure of quality, but these metrics are unfortunately not always well-correlated with the perceptual quality of the results. I find the results of this paper convincing, but I wish that this and other papers on this topic could find a way to evaluate their results more quantitatively. SUMMARY The problem and method are slightly incremental, but the several improvements over prior work make this paper a significant addition to the growing literature on neural style transfer. The paper is well-written and its experiments convincingly validate the benefits of the method. Overall I believe the paper would be a valuable addition to the conference. Pros - Simple modification to feedforward neural style transfer with several improvements over prior work - Strong qualitative results - Well-written - Open-source code has already been released Cons - Slightly incremental - Somewhat lacking in quantitative evaluation, but not any more so than prior work on this topic