Deep neural nets typically perform end-to-end backpropagation to learn the
weights, a procedure that creates synchronization constraints in the weight
update step across layers and is not biologically plausible. Recent advances in
unsupervised contrastive representation learning point to the question of
whether a learning algorithm can also be made local, that is, the updates of
lower layers do not directly depend on the computation of upper layers. While
Greedy InfoMax separately learns each block with a local objective, we found
that it consistently hurts readout accuracy in state-of-the-art unsupervised
contrastive learning algorithms, possibly due to the greedy objective as well
as gradient isolation. In this work, we discover that by overlapping local
blocks stacking on top of each other, we effectively increase the decoder depth
and allow upper blocks to implicitly send feedbacks to lower blocks. This
simple design closes the performance gap between local learning and end-to-end
contrastive learning algorithms for the first time. Aside from standard
ImageNet experiments, we also show results on complex downstream tasks such as
object detection and instance segmentation directly using readout features.

本文针对现有无监督对比学习算法存在的局部学习和全局学习性能差异的问题，提出了一种新的解决方案，即将本地块重叠并堆叠在一起以增加解码器深度，并为上层块隐含地向下层块发送反馈。通过在 ImageNet 中的实验以及直接使用 readout 特征进行对象检测和实例分割等复杂下游任务，我们发现该方法可有效处理局部学习和端到端对比学习算法之间的性能差距。