  The recent success of learning-based algorithms can be greatly attributed to
the immense amount of annotated data used for training. Yet, many datasets lack
annotations due to the high costs associated with labeling, resulting in
degraded performances of deep learning methods. Self-supervised learning is
frequently adopted to mitigate the reliance on massive labeled datasets since
it exploits unlabeled data to learn relevant feature representations. In this
work, we propose SS-StyleGAN, a self-supervised approach for image annotation
and classification suitable for extremely small annotated datasets. This novel
framework adds self-supervision to the StyleGAN architecture by integrating an
encoder that learns the embedding to the StyleGAN latent space, which is
well-known for its disentangled properties. The learned latent space enables
the smart selection of representatives from the data to be labeled for improved
classification performance. We show that the proposed method attains strong
classification results using small labeled datasets of sizes 50 and even 10. We
demonstrate the superiority of our approach for the tasks of COVID-19 and liver
tumor pathology identification.
