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Image transformation, a class of vision and graphics problems whose goal isto learn the mapping between an input image and an output image, developsrapidly in the context of deep neural networks. In Computer Vision (CV), manyproblems can be regarded as the image transformation task, e.g., semanticsegmentation and style transfer. These works have different topics andmotivations, making the image transformation task flourishing. Some surveysonly review the research on style transfer or image-to-image translation, allof which are just a branch of image transformation. However, none of thesurveys summarize those works together in a unified framework to our bestknowledge. This paper proposes a novel learning framework including Independentlearning, Guided learning, and Cooperative learning, called the IGC learningframework. The image transformation we discuss mainly involves the generalimage-to-image translation and style transfer about deep neural networks. Fromthe perspective of this framework, we review those subtasks and give a unifiedinterpretation of various scenarios. We categorize related subtasks about theimage transformation according to similar development trends. Furthermore,experiments have been performed to verify the effectiveness of IGC learning.Finally, new research directions and open problems are discussed for futureresearch.

Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information