Evolving Losses for Video Representation Learning

We present a new method to learn video representations from unlabeled data. We formulate our unsupervised representation learning as a multi-modal, multi-task learning problem. We also introduce the concept of finding a better loss function to train such multi-task multi-modal representation space using an evolutionary algorithm; our method automatically searches over different combinations of loss functions capturing multiple (self-supervised) tasks and modalities