StrokeNet: A Neural Painting Environment
We*ve seen tremendous success of image generating models these years. Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes. To imitate human drawing, interactions between the environment and the agent is required to allow trials. However, the environment is usually non-differentiable, leading to slow convergence and massive computation. In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation. We present StrokeNet, a novel model where the agent is trained upon a well-crafted neural approximation of the painting environment. With this approach, our agent was able to learn to write characters such as MNIST digits faster than reinforcement learning approaches in an unsupervised manner. Our primary contribution is the neural simulation of a real-world environment. Furthermore, the agent trained with the emulated environment is able to directly transfer its skills to real-world software.
 The paper proposes to use a differentiable drawing environment to synthesize images and provides information about some initial experiments. Not yet great about this paper: - the paper feels premature: There is a nice idea, but restricting the drawing environment to be - Some of the choices in the paper are a bit surprising, e.g. the lines in the drawing method are restricted to be at most 16 points long. If you look at real drawing data (e.g. the quickdraw dataset: https://quickdraw.withgoogle.com/data) you will find that users draw much longer lines typically. EDIT: the new version of the paper is much better but still feels like a bit incomplete. I personally would prefer a more complete evaluation and discussion of the proposed method. - the entire evaluation of this paper is purely qualitative (and that is not quite very convincing either). I feel it would be important for this paper to add some quantitative measure of quality. E.g. train an MNIST recognizer synthesized data and compare that to a recognizer trained on the original MNIST data. - a proper discussion of how the proposed environment is different from the environment proposed by Ganin et al (Deepmind*s SPIRAL) Minor comments: - abstract: why is it like "dreaming" -> I do agree with the rest of that statement, but I don*t see the connection to dreaming - abstract: "upper agent" -> is entirely unclear here. - abstract: the footnote at the end of the abstract is at a strange location - introduction: and could thus -> and can thus - introduction: second paragraph - it would be good to add some citations to this paragraph. - resulted image-> resulting image - the sentence: "We can generate....data is cheap" - is quite unclear to me at this time. Most of it becomes clearer later in the paper - but I feel it would be good to put this into proper context here (or not mention it) - we obtained -> we obtain - called a generator -> call a generator - the entire last paragraph on the first page is completely unclear to me when reading it here. - equations 1, 2: it*s unclear whether coordinates are absolute or relative coordinates. - fig 1: it*s very confusing that the generator, that is described first is represented at the right. - sec 3.2 - first line: wrong figure reference - you refer to fig 2 - but probably mean fig 1 - page 3 bottom: by appending the encoded color and radius data we have a feature with shape 64x64xn -> I don*t quite see how this is true. The image was 64x64 -> and I don*t quite understand why you have a color/radius for each pixel. - sec 3.3 - it seem sthat there is a partial sentence missing - sec 3.4 - is it relevant to the rest of the paper that the web application exists (and how it was implemented). - fig 2 / fig 3: these figures are very hard to read. Maybe inverting the images would help. Also fig 3 has very little value.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
StrokeNet: A Neural Painting Environment
We*ve seen tremendous success of image generating models these years. Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes. To imitate human drawing, interactions between the environment and the agent is required to allow trials. However, the environment is usually non-differentiable, leading to slow convergence and massive computation. In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation. We present StrokeNet, a novel model where the agent is trained upon a well-crafted neural approximation of the painting environment. With this approach, our agent was able to learn to write characters such as MNIST digits faster than reinforcement learning approaches in an unsupervised manner. Our primary contribution is the neural simulation of a real-world environment. Furthermore, the agent trained with the emulated environment is able to directly transfer its skills to real-world software.
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 proposes to use a differentiable drawing environment to synthesize images and provides information about some initial experiments. Not yet great about this paper: - the paper feels premature: There is a nice idea, but restricting the drawing environment to be - Some of the choices in the paper are a bit surprising, e.g. the lines in the drawing method are restricted to be at most 16 points long. If you look at real drawing data (e.g. the quickdraw dataset: https://quickdraw.withgoogle.com/data) you will find that users draw much longer lines typically. EDIT: the new version of the paper is much better but still feels like a bit incomplete. I personally would prefer a more complete evaluation and discussion of the proposed method. - the entire evaluation of this paper is purely qualitative (and that is not quite very convincing either). I feel it would be important for this paper to add some quantitative measure of quality. E.g. train an MNIST recognizer synthesized data and compare that to a recognizer trained on the original MNIST data. - a proper discussion of how the proposed environment is different from the environment proposed by Ganin et al (Deepmind*s SPIRAL) Minor comments: - abstract: why is it like "dreaming" -> I do agree with the rest of that statement, but I don*t see the connection to dreaming - abstract: "upper agent" -> is entirely unclear here. - abstract: the footnote at the end of the abstract is at a strange location - introduction: and could thus -> and can thus - introduction: second paragraph - it would be good to add some citations to this paragraph. - resulted image-> resulting image - the sentence: "We can generate....data is cheap" - is quite unclear to me at this time. Most of it becomes clearer later in the paper - but I feel it would be good to put this into proper context here (or not mention it) - we obtained -> we obtain - called a generator -> call a generator - the entire last paragraph on the first page is completely unclear to me when reading it here. - equations 1, 2: it*s unclear whether coordinates are absolute or relative coordinates. - fig 1: it*s very confusing that the generator, that is described first is represented at the right. - sec 3.2 - first line: wrong figure reference - you refer to fig 2 - but probably mean fig 1 - page 3 bottom: by appending the encoded color and radius data we have a feature with shape 64x64xn -> I don*t quite see how this is true. The image was 64x64 -> and I don*t quite understand why you have a color/radius for each pixel. - sec 3.3 - it seem sthat there is a partial sentence missing - sec 3.4 - is it relevant to the rest of the paper that the web application exists (and how it was implemented). - fig 2 / fig 3: these figures are very hard to read. Maybe inverting the images would help. Also fig 3 has very little value.
assistant
 The paper proposes to use a differentiable drawing environment to synthesize images and provides information about some initial experiments. Not yet great about this paper: - the paper feels premature: There is a nice idea, but restricting the drawing environment to be - Some of the choices in the paper are a bit surprising, e.g. the lines in the drawing method are restricted to be at most 16 points long. If you look at real drawing data (e.g. the quickdraw dataset: https://quickdraw.withgoogle.com/data) you will find that users draw much longer lines typically. EDIT: the new version of the paper is much better but still feels like a bit incomplete. I personally would prefer a more complete evaluation and discussion of the proposed method. - the entire evaluation of this paper is purely qualitative (and that is not quite very convincing either). I feel it would be important for this paper to add some quantitative measure of quality. E.g. train an MNIST recognizer synthesized data and compare that to a recognizer trained on the original MNIST data. - a proper discussion of how the proposed environment is different from the environment proposed by Ganin et al (Deepmind*s SPIRAL) Minor comments: - abstract: why is it like "dreaming" -> I do agree with the rest of that statement, but I don*t see the connection to dreaming - abstract: "upper agent" -> is entirely unclear here. - abstract: the footnote at the end of the abstract is at a strange location - introduction: and could thus -> and can thus - introduction: second paragraph - it would be good to add some citations to this paragraph. - resulted image-> resulting image - the sentence: "We can generate....data is cheap" - is quite unclear to me at this time. Most of it becomes clearer later in the paper - but I feel it would be good to put this into proper context here (or not mention it) - we obtained -> we obtain - called a generator -> call a generator - the entire last paragraph on the first page is completely unclear to me when reading it here. - equations 1, 2: it*s unclear whether coordinates are absolute or relative coordinates. - fig 1: it*s very confusing that the generator, that is described first is represented at the right. - sec 3.2 - first line: wrong figure reference - you refer to fig 2 - but probably mean fig 1 - page 3 bottom: by appending the encoded color and radius data we have a feature with shape 64x64xn -> I don*t quite see how this is true. The image was 64x64 -> and I don*t quite understand why you have a color/radius for each pixel. - sec 3.3 - it seem sthat there is a partial sentence missing - sec 3.4 - is it relevant to the rest of the paper that the web application exists (and how it was implemented). - fig 2 / fig 3: these figures are very hard to read. Maybe inverting the images would help. Also fig 3 has very little value.