We explore a new domain of learning to infer user interface attributes that helps developers automate the process of user interface implementation. Concretely, given an input image created by a designer, we learn to infer its implementation which when rendered, looks visually the same as the input image. To achieve this, we take a black box rendering engine and a set of attributes it supports (e.g., colors, border radius, shadow or text properties), use it to generate a suitable synthetic training dataset, and then train specialized neural models to predict each of the attribute values. To improve pixel-level accuracy, we also use imitation learning to train a neural policy that refines the predicted attribute values by learning to compute the similarity of the original and rendered images in their attribute space, rather than based on the difference of pixel values. We instantiate our approach to the task of inferring Android Button attribute values and achieve 92.5% accuracy on a dataset consisting of real-world Google Play Store applications.
Authors proposed an algorithm to predict the attribute of GUI elements from rasterized design images. The problem is separated into two steps. The first step is to predict initial values of the attributes (border width, color, padding etc) from the image where the type of UI element and set of attributes are already known. Authors designed a typical convolutional DNN for each of the attributes. The second step is to learn a policy \pi to iteratively adjust one attribute a time until the final rendering matches input image pixel-perfect. 
Authors conducted the experiment with large synthetic data set of Android buttons, and evaluate the performance with a held-out synthetic set as well as a 110 hand crafted real world buttons set sampled from apps in Google Play App Store. Several variations of the same model were compared. The result showed that network structure, padding strategy (this is a bit unexpected), background type and color selection strategy all affect the accuracy significantly. 
Reviewer has concern about the scope and application value of the problem as a research paper. A number of key prior assumptions have to be made to let the algorithm work: the type of the UI element need to be known; the list of attributes and their value ranges need to be fixed beforehand and each of the attribute demands a DNN; the refinement iteration has the actual rendering in the loop which could be costly on current App development platforms. 
Feedback questions.
1) The abstract mentioned vector image as input but the main body only discussed rasterized images.
2) Since the rendering process could be costly, it's useful to discuss the speed of convergence in the attribute value adjustment iterations.
3) Reviewer is interested in the nature of the error (7.5% loss) but it's not discussed.
4) In related work, authors mentioned the REINFORCE algorithm by Williams et al 1992 is expensive. It could help the reader if a brief explanation of why it's expensive is provided.
5) In Section 3 Background, authors mentioned that the attributes are not independent of each other, which is a major challenge. Reviewer would like to see some discussion or experiment data on how this affects the process and how did the current algorithm address it.
6) It's a bit surprise that color clipping method has a big impact on accuracy. Some examples could have helped the reviewer understand it.
7) In Section 4.2 first paragraph, it seems that the user of the algorithm need to set the [-c, c] clipping values manually per feature. This sounds like quite some prior knowledge and hand-tuning.
8) In Section 4.2. Second paragraph, Reviewer can understand the nessesity of additive and subtractive operations, but why multiplication?
9) In the equation to the end of page 5, do we need an extra outer bracket for the denominator? By the way, the equations should be numbered for easier reference.
10) The task of predicting Android button attributes, while practical, seems over-simplified. Reviewer suggests at least experiment with a set of common UI elements to proof the horizontal performance.
11) In Section 5.1, Reviewer respect the experiment results but doesn't understand why solid color background provides the best variety but screenshots don't. May need more analysis and explanation.
12) In Table 1, the first line for variant (C) also looks pretty good, or even better than core on the Android app store dataset.
13) In Section 5.1, the effect of color clipping selection seems very specific to applications with a fixed color palette. While this is indeed the majority, this prior knowledge need to be speciifed clearly by saying it's tailored towards such applications (or use more examples to proof that's not the case).
14) In Table 2: Pixel Sim's performance on Best Prediction Initialization seems pretty good, and Reviewer believes this is the more practical scenario. Is a more complicated Siamese Network justified?