  We present a new method to learn video representations from large-scale
unlabeled video data. Ideally, this representation will be generic and
transferable, directly usable for new tasks such as action recognition and zero
or few-shot learning. We formulate unsupervised representation learning as a
multi-modal, multi-task learning problem, where the representations are shared
across different modalities via distillation. Further, we introduce the concept
of loss function evolution by using an evolutionary search algorithm to
automatically find optimal combination of loss functions capturing many
(self-supervised) tasks and modalities. Thirdly, we propose an unsupervised
representation evaluation metric using distribution matching to a large
unlabeled dataset as a prior constraint, based on Zipf's law. This unsupervised
constraint, which is not guided by any labeling, produces similar results to
weakly-supervised, task-specific ones. The proposed unsupervised representation
learning results in a single RGB network and outperforms previous methods.
Notably, it is also more effective than several label-based methods (e.g.,
ImageNet), with the exception of large, fully labeled video datasets.
