mlsanityData to 7 Oct 2026

DeDrift: Robust Similarity Search under Content Drift

arXiv 2308.02752 ↗First posted 5 Aug 2023cs.CVcited by 11

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Abstract (arXiv)

The statistical distribution of content uploaded and searched on media sharing sites changes over time due to seasonal, sociological and technical factors. We investigate the impact of this "content drift" for large-scale similarity search tools, based on nearest neighbor search in embedding space. Unless a costly index reconstruction is performed frequently, content drift degrades the search accuracy and efficiency. The degradation is especially severe since, in general, both the query and database distributions change. We introduce and analyze real-world image and video datasets for which temporal information is available over a long time period. Based on the learnings, we devise DeDrift, a method that updates embedding quantizers to continuously adapt large-scale indexing structures on-the-fly. DeDrift almost eliminates the accuracy degradation due to the query and database content drift while being up to 100x faster than a full index reconstruction.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 21 of its 50 references that are in the map. First posted on arXiv 5 Aug 2023.

Builds on

21 of 50 references.

References · 21

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9 more
  1. 2021The 2021 Image Similarity Dataset and Challengecited by 44
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  3. 2021FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Searchcited by 36
  4. 2020Similarity Search for Efficient Active Learning and Search of Rare Conceptscited by 24
  5. 2019Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Searchcited by 19
  6. 2022TPU-KNN: K Nearest Neighbor Search at Peak FLOP/scited by 18
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  9. 2020K-means for Evolving Data Streamscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 1.

11 papers.