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 there are no pair annotations available. In this work, we present a rigorous and comprehensive study on inter-image invariance learning from three main constituting components: pseudo-label maintenance, sampling strategy, and decision boundary design. Through carefully-designed comparisons and analysis, we propose a unified and generic framework that supports the integration of unsupervised intra- and 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. Codes will be released at https://github.com/open-mmlab/OpenSelfSup.
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