mlsanityData to 14 Oct 2026

Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning

arXiv 2112.04607 ↗First posted 8 Dec 2021cs.CVcited by 7

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Abstract (arXiv)

We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluster images by pulling the embedding of a query image to be closer to its nearest neighbors (NNs). Since most NNs are close to the query by design, the averaging may not affect the embedding of the query much. On the other hand, far away NNs may not be semantically related to the query. We generalize the mean-shift idea by constraining the search space of NNs using another source of knowledge so that NNs are far from the query while still being semantically related. We show that our method (1) outperforms MSF in SSL setting when the constraint utilizes a different augmentation of an image from the previous epoch, and (2) outperforms PAWS in semi-supervised setting with less training resources when the constraint ensures that the NNs have the same pseudo-label as the query.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 65 of its 101 references that are in the map. First posted on arXiv 8 Dec 2021.

Builds on

65 of 101 references.

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7 papers.