  Self-supervised learning (SSL), in particular contrastive learning, has made
great progress in recent years. However, a common theme in these methods is
that they inherit the learning paradigm from the supervised deep learning
scenario. Current SSL methods are often pretrained for many epochs on
large-scale datasets using high resolution images, which brings heavy
computational cost and lacks flexibility. In this paper, we demonstrate that
the learning paradigm for SSL should be different from supervised learning and
the information encoded by the contrastive loss is expected to be much less
than that encoded in the labels in supervised learning via the cross entropy
loss. Hence, we propose scaled-down self-supervised learning (S3L), which
include 3 parts: small resolution, small architecture and small data. On a
diverse set of datasets, SSL methods and backbone architectures, S3L achieves
higher accuracy consistently with much less training cost when compared to
previous SSL learning paradigm. Furthermore, we show that even without a large
pretraining dataset, S3L can achieve impressive results on small data alone.
Our code has been made publically available at
https://github.com/CupidJay/Scaled-down-self-supervised-learning.
