Web: http://arxiv.org/abs/2201.11736

Jan. 28, 2022, 2:10 a.m. | David T. Hoffmann, Nadine Behrmann, Juergen Gall, Thomas Brox, Mehdi Noroozi

cs.CV updates on arXiv.org arxiv.org

This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a
new member in the family of InfoNCE losses that preserves a ranked ordering of
positive samples. In contrast to the standard InfoNCE loss, which requires a
strict binary separation of the training pairs into similar and dissimilar
samples, RINCE can exploit information about a similarity ranking for learning
a corresponding embedding space. We show that the proposed loss function learns
favorable embeddings compared to the standard InfoNCE whenever at least …

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