pix2code: Generating Code from a Graphical User Interface Screenshot
Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).
 This paper proposed an end-to-end network that generates computer tokens from a single GUI screenshot as input. Even though the introduced dataset in this paper is interesting, there are some issues: - On the model side, this paper used the same architecture as the Karpathy & Fei-Fei (2015). As a result, in my view, the paper has limited novelty and originality. - Experiments: The experiments are not enough at all. More specifically, the paper didn*t establish any baseline to show the difficulty of this problem and the dataset. Without a reasonable baseline, it is hard to see how difficult is this dataset and this problem and can*t say anything about the significance of this problem in this paper. - The related works: Some recent papers in program synthesis are missing and should have been included in this paper such as: RobustFill: Neural Program Learning under Noisy I/O, ICML 2017 Some other comments: - In the last paragraph of section 3 and first paragraph section 3.1, the authors mentioned that "...CNN to perform unsupervised feature learning...". In my view, it is not a correct statement to call the feature extraction from a CNN unsupervised feature learning as the referred CNNs in this paper are trained in a supervised manner and the network in this paper is trained using supervised learning as well. Unfortunately, At this point, I do not see a sufficient contribution to warrant publication in ICLR.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
pix2code: Generating Code from a Graphical User Interface Screenshot
Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).
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 proposed an end-to-end network that generates computer tokens from a single GUI screenshot as input. Even though the introduced dataset in this paper is interesting, there are some issues: - On the model side, this paper used the same architecture as the Karpathy & Fei-Fei (2015). As a result, in my view, the paper has limited novelty and originality. - Experiments: The experiments are not enough at all. More specifically, the paper didn*t establish any baseline to show the difficulty of this problem and the dataset. Without a reasonable baseline, it is hard to see how difficult is this dataset and this problem and can*t say anything about the significance of this problem in this paper. - The related works: Some recent papers in program synthesis are missing and should have been included in this paper such as: RobustFill: Neural Program Learning under Noisy I/O, ICML 2017 Some other comments: - In the last paragraph of section 3 and first paragraph section 3.1, the authors mentioned that "...CNN to perform unsupervised feature learning...". In my view, it is not a correct statement to call the feature extraction from a CNN unsupervised feature learning as the referred CNNs in this paper are trained in a supervised manner and the network in this paper is trained using supervised learning as well. Unfortunately, At this point, I do not see a sufficient contribution to warrant publication in ICLR.
assistant
 This paper proposed an end-to-end network that generates computer tokens from a single GUI screenshot as input. Even though the introduced dataset in this paper is interesting, there are some issues: - On the model side, this paper used the same architecture as the Karpathy & Fei-Fei (2015). As a result, in my view, the paper has limited novelty and originality. - Experiments: The experiments are not enough at all. More specifically, the paper didn*t establish any baseline to show the difficulty of this problem and the dataset. Without a reasonable baseline, it is hard to see how difficult is this dataset and this problem and can*t say anything about the significance of this problem in this paper. - The related works: Some recent papers in program synthesis are missing and should have been included in this paper such as: RobustFill: Neural Program Learning under Noisy I/O, ICML 2017 Some other comments: - In the last paragraph of section 3 and first paragraph section 3.1, the authors mentioned that "...CNN to perform unsupervised feature learning...". In my view, it is not a correct statement to call the feature extraction from a CNN unsupervised feature learning as the referred CNNs in this paper are trained in a supervised manner and the network in this paper is trained using supervised learning as well. Unfortunately, At this point, I do not see a sufficient contribution to warrant publication in ICLR.