DeDrift: Robust Similarity Search under Content Drift
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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
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Citing papers per year: 2024: 5; 2025: 5; 2026: 1.
11 papers.
- 2024The Faiss librarycited by 370
- 2024TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarkscited by 29
- 2025Inference-time sparse attention with asymmetric indexingcited by 11
- 2024Locally-Adaptive Quantization for Streaming Vector Searchcited by 10
- 2025Quake: Adaptive Indexing for Vector Searchcited by 8
- 2024Incremental IVF Index Maintenance for Streaming Vector Searchcited by 7
- 2025Cracking Vector Search Indexescited by 3
- 2025Lossless Compression of Vector IDs for Approximate Nearest Neighbor Searchcited by 1
- 2024Vector search with small radiusescited by 0
- 2025Individualized non-uniform quantization for vector searchcited by 0
- 2026A Power Law in Logarithm's Clothing: On the Scalability of Graph-Based Vector Searchcited by 0
- 2026A Power Law in Logarithm's Clothing: On the Scalability of Graph-Based Vector Search2 Sep 2026
- 2025Individualized non-uniform quantization for vector search22 Sep 2025
- 2025Quake: Adaptive Indexing for Vector Search3 Jun 2025
- 2025Cracking Vector Search Indexes3 Mar 2025
- 2025Inference-time sparse attention with asymmetric indexing12 Feb 2025
- 2025Lossless Compression of Vector IDs for Approximate Nearest Neighbor Search16 Jan 2025
- 2024Incremental IVF Index Maintenance for Streaming Vector Search1 Nov 2024
- 2024TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks27 Jun 2024
- 2024Vector search with small radiuses16 Mar 2024
- 2024Locally-Adaptive Quantization for Streaming Vector Search3 Feb 2024
- 2024The Faiss library16 Jan 2024