CloudifierNet -- Deep Vision Models for Artificial Image Processing

TL;DR

This paper introduces CloudifierNet, a deep neural network pipeline designed for artificial scene detection in user interfaces, emphasizing its development, principles, and benchmarking against existing transfer-learning vision models.

Contribution

It presents a novel deep vision model architecture tailored for artificial scene analysis, with experimental benchmarking demonstrating its effectiveness.

Findings

Benchmarking shows competitive performance with state-of-the-art models.

The model effectively analyzes artificial scenes from user interfaces.

Deep learning can be adapted for artificial scene detection in UI analysis.

Abstract

Today, more and more, it is necessary that most applications and documents developed in previous or current technologies to be accessible online on cloud-based infrastructures. That is why the migration of legacy systems including their hosts of documents to new technologies and online infrastructures, using modern Artificial Intelligence techniques, is absolutely necessary. With the advancement of Artificial Intelligence and Deep Learning with its multitude of applications, a new area of research is emerging - that of automated systems development and maintenance. The underlying work objective that led to this paper aims to research and develop truly intelligent systems able to analyze user interfaces from various sources and generate real and usable inferences ranging from architecture analysis to actual code generation. One key element of such systems is that of artificial scene detection and analysis based on deep learning computer vision systems. Computer vision models and particularly deep directed acyclic graphs based on convolutional modules are generally constructed and trained based on natural images datasets. Due to this fact, the models will develop during the training process natural image feature detectors apart from the base graph modules that will learn basic primitive features. In the current paper, we will present the base principles of a deep neural pipeline for computer vision applied to artificial scenes (scenes generated by user interfaces or similar). Finally, we will present the conclusions based on experimental development and benchmarking against state-of-the-art transfer-learning implemented deep vision models.