Novel Framework for Generating Criminals Images Based on Textual Data Using Identity GANs
作者机构:Department of Computer ScienceFaculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer ScienceFaculty of Computers and InformationMenoufia UniversityMenoufiaEgypt
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2023年第76卷第7期
页 面:383-396页
核心收录:
学科分类:0402[教育学-心理学(可授教育学、理学学位)] 0303[法学-社会学] 0710[理学-生物学] 0711[理学-系统科学] 1002[医学-临床医学] 100210[医学-外科学(含:普外、骨外、泌尿外、胸心外、神外、整形、烧伤、野战外)] 0812[工学-计算机科学与技术(可授工学、理学学位)] 10[医学]
主 题:GAN deep learning text-to-image identity GAN
摘 要:Text-to-image generation is a vital task in different fields,such as combating crime and terrorism and quickly arresting *** several years,due to a lack of deep learning and machine learning resources,police officials required artists to draw the face of a *** methods of identifying criminals are inefficient and *** paper presented a new proposed hybrid model for converting the text into the nearest images,then ranking the produced images according to the available *** framework contains two main steps:generation of the image using an Identity Generative Adversarial Network(IGAN)and ranking of the images according to the available data using multi-criteria decision-making based on neutrosophic *** IGAN has the same architecture as the classical Generative Adversarial Networks(GANs),but with different modifications,such as adding a non-linear identity block,smoothing the standard GAN loss function by using a modified loss function and label smoothing,and using mini-batch *** model achieves efficient results in Inception Distance(FID)and inception score(IS)when compared with other architectures of GANs for generating images from *** IGAN achieves 42.16 as FID and 14.96 as *** it comes to ranking the generated images using Neutrosophic,the framework also performs well in the case of missing information and missing data.