  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 noisy
ranking information can be obtained or when the definition of positives and
negatives is blurry. We demonstrate this for a supervised classification task
with additional superclass labels and noisy similarity scores. Furthermore, we
show that RINCE can also be applied to unsupervised training with experiments
on unsupervised representation learning from videos. In particular, the
embedding yields higher classification accuracy, retrieval rates and performs
better in out-of-distribution detection than the standard InfoNCE loss.
