Self-Supervised Image Classification

This is the task of image classification using representations learnt with self-supervised learning. Self-supervised methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. One example of a loss function is an autoencoder based loss where the goal is reconstruction of an image pixel-by-pixel. A more popular recent example is a contrastive loss, which measure the similarity of sample pairs in a representation space, and where there can be a varying target instead of a fixed target to reconstruct (as in the case of autoencoders).

A common evaluation protocol is to train a linear classifier on top of (frozen) representations learnt by self-supervised methods. The leaderboards for the linear evaluation protocol can be found below. In practice, it is more common to fine-tune features on a downstream task. An alternative evaluation protocol therefore uses semi-supervised learning and finetunes on a % of the labels. The leaderboards for the finetuning protocol can be accessed here.

You may want to read some blog posts before reading the papers and checking the leaderboards:

• Contrastive Self-Supervised Learning - Ankesh Anand
• The Illustrated Self-Supervised Learning - Amit Chaudhary
• Self-supervised learning and computer vision - Jeremy Howard
• Self-Supervised Representation Learning - Lilian Weng

There is also Yann LeCun's talk at AAAI-20 which you can watch here (35:00+).

( Image credit: A Simple Framework for Contrastive Learning of Visual Representations )