What does it take to generate natural textures?
Natural image generation is currently one of the most actively explored fields in Deep Learning. Many approaches, e.g. for state-of-the-art artistic style transfer or natural texture synthesis, rely on the statistics of hierarchical representations in supervisedly trained deep neural networks. It is, however, unclear what aspects of this feature representation are crucial for natural image generation: is it the depth, the pooling or the training of the features on natural images? We here address this question for the task of natural texture synthesis and show that none of the above aspects are indispensable. Instead, we demonstrate that natural textures of high perceptual quality can be generated from networks with only a single layer, no pooling and random filters.
This paper provides an interesting analysis of the conditions which enable generation of natural looking textures. The results is quite surprising, and analysis is quite thorough. I do think the evaluation methods require more work, but as other reviewers mentioned this could be an interesting line of work moving forwards and does not take too much from this current paper which, I think, should be accepted.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
What does it take to generate natural textures?
Natural image generation is currently one of the most actively explored fields in Deep Learning. Many approaches, e.g. for state-of-the-art artistic style transfer or natural texture synthesis, rely on the statistics of hierarchical representations in supervisedly trained deep neural networks. It is, however, unclear what aspects of this feature representation are crucial for natural image generation: is it the depth, the pooling or the training of the features on natural images? We here address this question for the task of natural texture synthesis and show that none of the above aspects are indispensable. Instead, we demonstrate that natural textures of high perceptual quality can be generated from networks with only a single layer, no pooling and random filters.
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>



This paper provides an interesting analysis of the conditions which enable generation of natural looking textures. The results is quite surprising, and analysis is quite thorough. I do think the evaluation methods require more work, but as other reviewers mentioned this could be an interesting line of work moving forwards and does not take too much from this current paper which, I think, should be accepted.
assistant
This paper provides an interesting analysis of the conditions which enable generation of natural looking textures. The results is quite surprising, and analysis is quite thorough. I do think the evaluation methods require more work, but as other reviewers mentioned this could be an interesting line of work moving forwards and does not take too much from this current paper which, I think, should be accepted.