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Exploring Instance Relations for Unsupervised Feature Embedding
[article]
2021
arXiv
pre-print
Despite the great progress achieved in unsupervised feature embedding, existing contrastive learning methods typically pursue view-invariant representations through attracting positive sample pairs and repelling negative sample pairs in the embedding space, while neglecting to systematically explore instance relations. In this paper, we explore instance relations including intra-instance multi-view relation and inter-instance interpolation relation for unsupervised feature embedding.
arXiv:2105.03341v1
fatcat:zmd7hz32w5gnjb54elrtapg2ei