Abstract
In this talk, I will discuss how to learn representation for perception and action without using any manual supervision. First, I am going to discuss how we can learn ConvNets for vision in a completely unsupervised manner using auxiliary tasks. Specifically, I am going to demonstrate how spatial context in images and viewpoint changes in videos can be used to train visual representations. Next, I am going to talk about how we can use a robot to physically explore the world and learn visual representations for classification/recognition tasks. Finally, I am going to talk about how we can perform end-to-end learning for actions using self-supervision.