Classification for image creation

Abstract

Implementations disclosed herein provide systems and methods that use classification-based machine learning to generate perceptually-plausible content for a missing part (e.g., some or all) of an image. The machine learning model may be trained to generate content for the missing part that appears plausible by learning to generate content that cannot be distinguished from real image content, for example, using adversarial loss-based training. To generate the content, a probabilistic classifier may be used to select color attribute values (e.g., RGB values) for each pixel of the missing part of the image. To do so, a pixel color attribute is segmented into a number of bins (e.g., value ranges) that are used as classes. The classifier determines probabilities for each of the bins of a color attribute for each pixel and generates the content by selecting the bin having the highest probability for each color attribute for each pixel.