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Summer school lecture: Unsupervised Learning of Object Material and Shape from Images and Videos

When DNNs are trained on ground-truth information, they can reach or exceed human performance in many visual tasks. However, no equivalent to these massive labelled training sets exists during human visual development. I will talk about two projects …

ECVP talk: Learning about Shape, Material, and Illumination by Predicting Videos

Unsupervised deep learning provides a framework for understanding how brains learn rich scene representations without ground-truth world information. We rendered 10,000 videos of irregularly-shaped objects moving with random rotational axis, speed, …

Symposium talk: Unsupervised Learning Predicts Perception and Misperception of Materials

Presenting new results from two projects using unsupervised deep neural networks to learn about image structure from static or moving visual input. Such networks spontaneously learn to represent underlying scene factors, and can predict human gloss …