This project studies information representation for visual perception. As can be inferred from the numerous examples of visual illusions, visual perception is often ambiguous. This makes it important that perceptual inference is coupled with statements of uncertainty, that quantify whether the percept is ambiguous, or if there are several potentially valid explanations of the sensory data. Research is conducted by designing deep neural networks that take images, video, or point-cloud data as input. The networks predict percepts, and their associated uncertainties, using representations such as probability density functions, and ensembles. A major focus is the design of appropriate objective functions that facilitate learning while avoiding problems of overconfidence.