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Despite the great progress achieved in unsupervised feature embedding,existing contrastive learning methods typically pursue view-invariantrepresentations through attracting positive sample pairs and repelling negativesample pairs in the embedding space, while neglecting to systematically exploreinstance relations. In this paper, we explore instance relations includingintra-instance multi-view relation and inter-instance interpolation relationfor unsupervised feature embedding. Specifically, we embed intra-instancemulti-view relation by aligning the distribution of the distance between aninstance's different augmented samples and negative samples. We exploreinter-instance interpolation relation by transferring the ratio of informationfor image sample interpolation from pixel space to feature embedding space. Theproposed approach, referred to as EIR, is simple-yet-effective and can beeasily inserted into existing view-invariant contrastive learning basedmethods. Experiments conducted on public benchmarks for image classificationand retrieval report state-of-the-art or comparable performance.

Exploring Instance Relations for Unsupervised Feature Embedding