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It involves methods to be developed to replicate the capabilities of biological vision. The computer vision\u2019s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks\u2019 applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In\u2010depth analysis of deep generative adversarial network applications, namely, image\u2010to\u2010image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam\u2010based eye trackers, lumen center detection, query\u2010by\u2010string word, intermittently closed and open lakes and lagoons, and landslides.<\/jats:p>","DOI":"10.1155\/2021\/5541134","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T19:28:35Z","timestamp":1618946915000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Deep CNN and Deep GAN in Computational Visual Perception\u2010Driven Image Analysis"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8610-9629","authenticated-orcid":false,"given":"R.","family":"Nandhini Abirami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7598-1363","authenticated-orcid":false,"given":"P. 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