A critical analysis of self-supervision, or what we can learn from a single image

Asano YM., Rupprecht C., Vedaldi A.

Keywords: self-supervision, feature representation learning, CNN

TL;DR: We evaluate self-supervised feature learning methods and find that with sufficient data augmentation early layers can be learned using just one image. This is informative about self-supervision and the role of augmentations.

Abstract:

We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels. We show that three different and representative methods, BiGAN, RotNet and DeepCluster, can learn the first few layers of a convolutional network from a single image as well as using millions of images and manual labels, provided that strong data augmentation is used. However, for deeper layers the gap with manual supervision cannot be closed even if millions of unlabelled images are used for training.
We conclude that:
(1) the weights of the early layers of deep networks contain limited information about the statistics of natural images, that
(2) such low-level statistics can be learned through self-supervision just as well as through strong supervision, and that
(3) the low-level statistics can be captured via synthetic transformations instead of using a large image dataset.

Code: [ yukimasano/linear-probes](https://github.com/yukimasano/linear-probes) + [ 1 community implementation](https://paperswithcode.com/paper/?openreview=B1esx6EYvr)

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CIFAR-100](https://paperswithcode.com/dataset/cifar-100), [ImageNet](https://paperswithcode.com/dataset/imagenet)