  Self-supervised learning solves pretext prediction tasks that do not require
annotations to learn feature representations. For vision tasks, pretext tasks
such as predicting rotation, solving jigsaw are solely created from the input
data. Yet, predicting this known information helps in learning representations
useful for downstream tasks. However, recent works have shown that wider and
deeper models benefit more from self-supervised learning than smaller models.
To address the issue of self-supervised pre-training of smaller models, we
propose Distill-on-the-Go (DoGo), a self-supervised learning paradigm using
single-stage online knowledge distillation to improve the representation
quality of the smaller models. We employ deep mutual learning strategy in which
two models collaboratively learn from each other to improve one another.
Specifically, each model is trained using self-supervised learning along with
distillation that aligns each model's softmax probabilities of similarity
scores with that of the peer model. We conduct extensive experiments on
multiple benchmark datasets, learning objectives, and architectures to
demonstrate the potential of our proposed method. Our results show significant
performance gain in the presence of noisy and limited labels and generalization
to out-of-distribution data.
