Reverse Browser: Vector-Image-to-Code Generator
Automating the conversion of user interface design into code (image-to-code or image-to-UI) is an active area of software engineering research. However, the state-of-the-art solutions do not achieve high fidelity to the original design, as evidenced by benchmarks. In this work, I approach the problem differently: I use vector images instead of bitmaps as model input. I create several large datasets for training machine learning models. I evaluate the available array of Image Quality Assessment (IQA) algorithms and introduce a new, multi-scale metric. I then train a large open-weights model and discuss its limitations.
Code (0)
등록된 구현이 없습니다.
Tasks
Image Quality AssessmentSimilar Papers 제목 키워드 기반
Reinforcement learning guided fuzz testing for a browser's HTML rendering engine
Generation-based fuzz testing can uncover various bugs and security vulnerabilities. However, compared to mutation-based fuzz testing, it takes much longer to develop a well-balanced generator that produces good test cas…
reinforcement-learningTest Case CreationSemi-supervised Image Attribute Editing using Generative Adversarial Networks
Image attribute editing is a challenging problem that has been recently studied by many researchers using generative networks. The challenge is in the manipulation of selected attributes of images while preserving the ot…
AttributeImage ReconstructionInternal APIs Are All You Need: Shadow APIs, Shared Discovery, and the Case Against Browser-First Agent Architectures
Autonomous agents increasingly interact with the web, yet most websites remain designed for human browsers -- a fundamental mismatch that the emerging ``Agentic Web'' must resolve. Agents must repeatedly browse pages, in…
Recurrent Neural Networks for Fuzz Testing Web Browsers
Generation-based fuzzing is a software testing approach which is able to discover different types of bugs and vulnerabilities in software. It is, however, known to be very time consuming to design and fine tune classical…
Model Selectionsoftware testingStyle Separation and Synthesis via Generative Adversarial Networks
Style synthesis attracts great interests recently, while few works focus on its dual problem "style separation". In this paper, we propose the Style Separation and Synthesis Generative Adversarial Network (S3-GAN) to sim…
Generative Adversarial NetworkObject