![]() ![]() For image generation applications, researchers have formulated the task in such a way that the model is supposed to generate an image given another image. It is virtually impossible to come up with a Deep learning model without formally defining the task it is supposed to perform. Understanding image to image translation taskīefore we jump into learning more about what an image-to-image translation task is, we must first understand how machine learning problems are formulated. 1) Difficulty in the generation of high-resolution images 2) Lack of details and realistic textures in generated high-resolution pictures. This paper addresses the 2 main concerns of vanilla adversarial learning i.e. These include increased computational complexity for higher-dimensional images as well as a lack of ability to generalize as image size increases. The current state of art image-to-image translation Deep learning models are not able to generate high-resolution images because of various limitations. ![]() This task can be modeled as an image-to-image translation model that can be used to create vivid virtual worlds by simply training it on new datasets.Ī simple example of an image-to-image translation task where the edges of an object are fed as an input to the model and the model generates the actual object. If we were able to render photo-realistic images using a model learned from data, we could turn the process of graphics rendering into a model learning and inference problem. The algorithms which we use currently for the task are effective but expensive. Photo-realistic image rendering using standard graphics techniques requires realistic simulation of geometry and light. ![]()
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