Recent self-supervised contrastive methods have been able to produce
impressive transferable visual representations by learning to be invariant to
different data augmentations. However, these methods implicitly assume a
particular set of representational invariances (e.g., invariance to color), and
can perform poorly when a downstream task violates this assumption (e.g.,
distinguishing red vs. yellow cars). We introduce a contrastive learning
framework which does not require prior knowledge of specific, task-dependent
invariances. Our model learns to capture varying and invariant factors for
visual representations by constructing separate embedding spaces, each of which
is invariant to all but one augmentation. We use a multi-head network with a
shared backbone which captures information across each augmentation and alone
outperforms all baselines on downstream tasks. We further find that the
concatenation of the invariant and varying spaces performs best across all
tasks we investigate, including coarse-grained, fine-grained, and few-shot
downstream classification tasks, and various data corruptions.

本文提出了一种对比学习方法，该方法不需要先前知道特定于任务的不变性，使用多头网络构建不同的嵌入空间来捕捉可变及不变的因素 以提高视觉表示的效果，并发现不变空间和变化空间的连接在包括数据破坏等各种下游分类任务中表现最佳。