  Recent single image unsupervised representation learning techniques show
remarkable success on a variety of tasks. The basic principle in these works is
instance discrimination: learning to differentiate between two augmented
versions of the same image and a large batch of unrelated images. Networks
learn to ignore the augmentation noise and extract semantically meaningful
representations. Prior work uses artificial data augmentation techniques such
as cropping, and color jitter which can only affect the image in superficial
ways and are not aligned with how objects actually change e.g. occlusion,
deformation, viewpoint change. In this paper, we argue that videos offer this
natural augmentation for free. Videos can provide entirely new views of
objects, show deformation, and even connect semantically similar but visually
distinct concepts. We propose Video Noise Contrastive Estimation, a method for
using unlabeled video to learn strong, transferable single image
representations. We demonstrate improvements over recent unsupervised single
image techniques, as well as over fully supervised ImageNet pretraining, across
a variety of temporal and non-temporal tasks. Code and the Random Related Video
Views dataset are available at https://www.github.com/danielgordon10/vince
