In this talk I will show how complex visual inference tasks can be performed with Deep-Learning, in a totally unsupervised way, by training on a single image — the test image itself. The strong recurrence of information inside a single natural image provides powerful internal examples which suffice for self-supervision of Deep-Networks, without any prior examples or training data. This new paradigm gives rise to true “zero-shot learning”. I will show the power of this approach to a variety of problems, including super-resolution, image-segmentation, transparent layer separation, image-dehazing, image-retargeting, and more.
I will further show how self-supervision can be used for “mind-reading” from very little fMRI data.