  Self-supervised learning methods are gaining increasing traction in computer
vision due to their recent success in reducing the gap with supervised
learning. In natural language processing (NLP) self-supervised learning and
transformers are already the methods of choice. The recent literature suggests
that the transformers are becoming increasingly popular also in computer
vision. So far, the vision transformers have been shown to work well when
pretrained either using a large scale supervised data or with some kind of
co-supervision, e.g. in terms of teacher network. These supervised pretrained
vision transformers achieve very good results in downstream tasks with minimal
changes. In this work we investigate the merits of self-supervised learning for
pretraining image/vision transformers and then using them for downstream
classification tasks. We propose Self-supervised vIsion Transformers (SiT) and
discuss several self-supervised training mechanisms to obtain a pretext model.
The architectural flexibility of SiT allows us to use it as an autoencoder and
work with multiple self-supervised tasks seamlessly. We show that a pretrained
SiT can be finetuned for a downstream classification task on small scale
datasets, consisting of a few thousand images rather than several millions. The
proposed approach is evaluated on standard datasets using common protocols. The
results demonstrate the strength of the transformers and their suitability for
self-supervised learning. We outperformed existing self-supervised learning
methods by large margin. We also observed that SiT is good for few shot
learning and also showed that it is learning useful representation by simply
training a linear classifier on top of the learned features from SiT.
Pretraining, finetuning, and evaluation codes will be available under:
https://github.com/Sara-Ahmed/SiT.
