  This paper is concerned with self-supervised learning for small models. The
problem is motivated by our empirical studies that while the widely used
contrastive self-supervised learning method has shown great progress on large
model training, it does not work well for small models. To address this
problem, we propose a new learning paradigm, named SElf-SupErvised Distillation
(SEED), where we leverage a larger network (as Teacher) to transfer its
representational knowledge into a smaller architecture (as Student) in a
self-supervised fashion. Instead of directly learning from unlabeled data, we
train a student encoder to mimic the similarity score distribution inferred by
a teacher over a set of instances. We show that SEED dramatically boosts the
performance of small networks on downstream tasks. Compared with
self-supervised baselines, SEED improves the top-1 accuracy from 42.2% to 67.6%
on EfficientNet-B0 and from 36.3% to 68.2% on MobileNet-v3-Large on the
ImageNet-1k dataset.
