  For a robot deployed in the world, it is desirable to have the ability of
autonomous learning to improve its initial pre-set knowledge. We formalize this
as a bootstrapped self-supervised learning problem where a system is initially
bootstrapped with supervised training on a labeled dataset and we look for a
self-supervised training method that can subsequently improve the system over
the supervised training baseline using only unlabeled data. In this work, we
leverage temporal consistency between frames in monocular video to perform this
bootstrapped self-supervised training. We show that a well-trained
state-of-the-art semantic segmentation network can be further improved through
our method. In addition, we show that the bootstrapped self-supervised training
framework can help a network learn depth estimation better than pure supervised
training or self-supervised training.
