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:
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 )
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Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge.
#3 best model for Self-Supervised Image Classification on ImageNet
This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos.