Incorporating long-range consistency in CNN-based texture generation
Gatys et al. (2015) showed that pair-wise products of features in a convolutional network are a very effective representation of image textures. We propose a simple modification to that representation which makes it possible to incorporate long-range structure into image generation, and to render images that satisfy various symmetry constraints. We show how this can greatly improve rendering of regular textures and of images that contain other kinds of symmetric structure. We also present applications to inpainting and season transfer.
The paper investigates a simple extension of Gatys et al. CNN-based texture descriptors for image generation. Similar to Gatys et al., the method uses as texture descriptor the empirical intra-channel correlation matrix of the CNN feature response at some layer of a deep network. Differently from Gatys et al., longer range correlations are measured by introducing a shift between the correlated feature responses, which translates in a simple modification of the original architecture. The idea is simple but has interesting effects on the generated textures and can be extended to transformations other than translation. While longer range correlations could be accounted for by considering the response of deeper CNN features in the original method by Gatys et al., the authors show that modelling them explicitly using shallower features is more effective, which is reasonable. An important limitation that this work shares with most of its peers is the lack of a principled quantitative evaluation protocol, such that judging the effectiveness of the approach remains almost entirely a qualitative affair. While this should not be considered a significant drawback of the paper due to the objective difficulty of solving this open issue, nevertheless it is somewhat limiting that no principled evaluation method could be devised and implemented. The authors suggest that, as future work, a possible evaluation method could be based on a classification task -- this is a potentially interesting approach that merits some further investigation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Incorporating long-range consistency in CNN-based texture generation
Gatys et al. (2015) showed that pair-wise products of features in a convolutional network are a very effective representation of image textures. We propose a simple modification to that representation which makes it possible to incorporate long-range structure into image generation, and to render images that satisfy various symmetry constraints. We show how this can greatly improve rendering of regular textures and of images that contain other kinds of symmetric structure. We also present applications to inpainting and season transfer.
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>



The paper investigates a simple extension of Gatys et al. CNN-based texture descriptors for image generation. Similar to Gatys et al., the method uses as texture descriptor the empirical intra-channel correlation matrix of the CNN feature response at some layer of a deep network. Differently from Gatys et al., longer range correlations are measured by introducing a shift between the correlated feature responses, which translates in a simple modification of the original architecture. The idea is simple but has interesting effects on the generated textures and can be extended to transformations other than translation. While longer range correlations could be accounted for by considering the response of deeper CNN features in the original method by Gatys et al., the authors show that modelling them explicitly using shallower features is more effective, which is reasonable. An important limitation that this work shares with most of its peers is the lack of a principled quantitative evaluation protocol, such that judging the effectiveness of the approach remains almost entirely a qualitative affair. While this should not be considered a significant drawback of the paper due to the objective difficulty of solving this open issue, nevertheless it is somewhat limiting that no principled evaluation method could be devised and implemented. The authors suggest that, as future work, a possible evaluation method could be based on a classification task -- this is a potentially interesting approach that merits some further investigation.
assistant
The paper investigates a simple extension of Gatys et al. CNN-based texture descriptors for image generation. Similar to Gatys et al., the method uses as texture descriptor the empirical intra-channel correlation matrix of the CNN feature response at some layer of a deep network. Differently from Gatys et al., longer range correlations are measured by introducing a shift between the correlated feature responses, which translates in a simple modification of the original architecture. The idea is simple but has interesting effects on the generated textures and can be extended to transformations other than translation. While longer range correlations could be accounted for by considering the response of deeper CNN features in the original method by Gatys et al., the authors show that modelling them explicitly using shallower features is more effective, which is reasonable. An important limitation that this work shares with most of its peers is the lack of a principled quantitative evaluation protocol, such that judging the effectiveness of the approach remains almost entirely a qualitative affair. While this should not be considered a significant drawback of the paper due to the objective difficulty of solving this open issue, nevertheless it is somewhat limiting that no principled evaluation method could be devised and implemented. The authors suggest that, as future work, a possible evaluation method could be based on a classification task -- this is a potentially interesting approach that merits some further investigation.