Many recent self-supervised frameworks for visual representation learning are
based on certain forms of Siamese networks. Such networks are conceptually
symmetric with two parallel encoders, but often practically asymmetric as
numerous mechanisms are devised to break the symmetry. In this work, we conduct
a formal study on the importance of asymmetry by explicitly distinguishing the
two encoders within the network -- one produces source encodings and the other
targets. Our key insight is keeping a relatively lower variance in target than
source generally benefits learning. This is empirically justified by our
results from five case studies covering different variance-oriented designs,
and is aligned with our preliminary theoretical analysis on the baseline.
Moreover, we find the improvements from asymmetric designs generalize well to
longer training schedules, multiple other frameworks and newer backbones.
Finally, the combined effect of several asymmetric designs achieves a
state-of-the-art accuracy on ImageNet linear probing and competitive results on
downstream transfer. We hope our exploration will inspire more research in
exploiting asymmetry for Siamese representation learning.

本论文探讨了利用不对称网络结构进行自监督表示学习的重要性，发现保持目标编码比源编码相对较低的方差通常对学习有利，并且在多个案例和数据集上得到了验证和实验结果的支持，可以在 ImageNet 线性探针上实现最新的精度，并在下游传输学习方面取得了有竞争力的结果。