Aralikatti, R.C.Sangeeth, S.V.Chandavarkar, B.R.2026-02-0620212021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, 2021, Vol., , p. -https://doi.org/10.1109/ICCCNT51525.2021.9580159https://idr.nitk.ac.in/handle/123456789/30224Deep learning has greatly revolutionized the ways in which computers tackle problems in vision, speech recognition, machine translation, etc., and has produced results which are almost inconceivable to conventional algorithms. Creative tasks such as fine arts and music composition, which were initially thought to be impossible to computers, are now possible. In this paper, we look at a particular class of problems called image-to-image translation problems and see how it can be leveraged to perform artistic image transformations. Generative Adversarial Networks (GANs) and related neural networks are particularly useful for this task. We explore some of the artistic image transformation tasks that deep learning can be used for and discuss the different machine learning architectures used, the results produced and the advancements made in literature towards tackling such tasks. © 2021 IEEE.CycleGANDeep LearningGenerative Adversarial Networks (GANs)Image ProcessingNeural Style Transfer