Contrastive learning has recently shown immense potential in unsupervised
visual representation learning. Existing studies in this track mainly focus on
intra-image invariance learning. The learning typically uses rich intra-image
transformations to construct positive pairs and then maximizes agreement using
a contrastive loss. The merits of inter-image invariance, conversely, remain
much less explored. One major obstacle to exploit inter-image invariance is
that it is unclear how to reliably construct inter-image positive pairs, and
further derive effective supervision from them since no pair annotations are
available. In this work, we present a comprehensive empirical study to better
understand the role of inter-image invariance learning from three main
constituting components: pseudo-label maintenance, sampling strategy, and
decision boundary design. To facilitate the study, we introduce a unified and
generic framework that supports the integration of unsupervised intra- and
inter-image invariance learning. Through carefully-designed comparisons and
analysis, multiple valuable observations are revealed: 1) online labels
converge faster and perform better than offline labels; 2) semi-hard negative
samples are more reliable and unbiased than hard negative samples; 3) a less
stringent decision boundary is more favorable for inter-image invariance
learning. With all the obtained recipes, our final model, namely InterCLR,
shows consistent improvements over state-of-the-art intra-image invariance
learning methods on multiple standard benchmarks. We hope this work will
provide useful experience for devising effective unsupervised inter-image
invariance learning. Code: this https URL

本研究通过对伪标签的维护、采样策略和决策边界的设计进行了综合实证研究，提出了一种统一的通用框架，支持无监督内部和跨图片不变性学习的集成。通过精心设计的比较和分析，我们揭示了多个有价值的观测结果，并展示了最终模型 InterCLR 在多个标准基准测试中对内部图像不变性学习方法的稳定改进。我们希望这项工作能为制定有效的无监督跨图片不变性学习提供有用的经验。