  Contrastive self-supervised learning (SSL) methods, such as MoCo and SimCLR,
have achieved great success in unsupervised visual representation learning.
They rely on a large number of negative pairs and thus require either large
memory banks or large batches. Some recent non-contrastive SSL methods, such as
BYOL and SimSiam, attempt to discard negative pairs and have also shown
remarkable performance. To avoid collapsed solutions caused by not using
negative pairs, these methods require non-trivial asymmetry designs. However,
in small data regimes, we can not obtain a sufficient number of negative pairs
or effectively avoid the over-fitting problem when negatives are not used at
all. To address this situation, we argue that negative pairs are still
important but one is generally sufficient for each positive pair. We show that
a simple Triplet-based loss (Trip) can achieve surprisingly good performance
without requiring large batches or asymmetry designs. Moreover, to alleviate
the over-fitting problem in small data regimes and further enhance the effect
of Trip, we propose a simple plug-and-play RandOm MApping (ROMA) strategy by
randomly mapping samples into other spaces and requiring these randomly
projected samples to satisfy the same relationship indicated by the triplets.
Integrating the triplet-based loss with random mapping, we obtain the proposed
method Trip-ROMA. Extensive experiments, including unsupervised representation
learning and unsupervised few-shot learning, have been conducted on ImageNet-1K
and seven small datasets. They successfully demonstrate the effectiveness of
Trip-ROMA and consistently show that ROMA can further effectively boost other
SSL methods. Code is available at https://github.com/WenbinLee/Trip-ROMA.
