Learning Resolution-independent Image Representations

摘要

Humans are well-known to be highly effective at comprehending continuous patterns within digital images. We present a collection of methods that enable analogous capabilities in deep neural networks. These methods train neural networks to represent images with continuous resolution-independent representations. They utilize an MCMC algorithm that directs attention during the learning phase to regions of the image that deviate from the current model. An encoding hypernetwork learns to generalize from a collection of images, such that it can effectively compute resolution-independent representations in constant time. These methods have immediate applications in super-resolution scaling of images, image compression, and secure image processing, and additionally suggest improved capabilities for image processing with neural networks in several future applications.