Training masked autoencoders for image inpainting

Abstract

The disclosure herein describes training an encoder network to inpaint images with masked portions. A primary encoding process is used to encode a visible portion of a masked input image into encoded token data. The encoded token data is then decoded into both pixel regression output and feature prediction output, wherein both outputs include inpainted image data associated with the masked portion of the masked input image. A pixel regression loss is determined using the pixel regression output and pixel data of an unmasked version of the masked input image. A feature prediction loss is determined using the feature prediction output and ground truth encoding output of the unmasked version of the masked input image. The primary encoding process is then trained using the pixel regression loss and the feature prediction loss, whereby the primary encoding process is trained to encode structural features of input images into encoded token data.