# DeDrift: Robust Similarity Search under Content Drift

arXiv: https://arxiv.org/abs/2308.02752 · HTML: https://mlsanity.com/paper/2308.02752/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 21 of 21)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [A Self-Supervised Descriptor for Image Copy Detection](https://mlsanity.com/paper/2202.10261/) (2022)
- [Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples](https://mlsanity.com/paper/2104.13963/) (2021)
- [Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph](https://mlsanity.com/paper/1707.00143/) (2017)
- [Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2205.03763/) (2022)
- [The 2021 Image Similarity Dataset and Challenge](https://mlsanity.com/paper/2106.09672/) (2021)
- [FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search](https://mlsanity.com/paper/2105.09613/) (2021)
- [Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors](https://mlsanity.com/paper/1802.02422/) (2018)
- [Similarity Search for Efficient Active Learning and Search of Rare Concepts](https://mlsanity.com/paper/2007.00077/) (2020)
- [Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search](https://mlsanity.com/paper/1903.01612/) (2019)
- [TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s](https://mlsanity.com/paper/2206.14286/) (2022)
- [OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries](https://mlsanity.com/paper/2211.12850/) (2022)
- [Online Product Quantization](https://mlsanity.com/paper/1711.10775/) (2017)
- [K-means for Evolving Data Streams](https://mlsanity.com/paper/2012.03628/) (2020)

## Built on by (most cited first, 11 of 11)

- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks](https://mlsanity.com/paper/2406.19380/) (2024)
- [Locally-Adaptive Quantization for Streaming Vector Search](https://mlsanity.com/paper/2402.02044/) (2024)
- [Inference-time sparse attention with asymmetric indexing](https://mlsanity.com/paper/2502.08246/) (2025)
- [Quake: Adaptive Indexing for Vector Search](https://mlsanity.com/paper/2506.03437/) (2025)
- [Incremental IVF Index Maintenance for Streaming Vector Search](https://mlsanity.com/paper/2411.00970/) (2024)
- [Cracking Vector Search Indexes](https://mlsanity.com/paper/2503.01823/) (2025)
- [Lossless Compression of Vector IDs for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2501.10479/) (2025)
- [Vector search with small radiuses](https://mlsanity.com/paper/2403.10746/) (2024)
- [Individualized non-uniform quantization for vector search](https://mlsanity.com/paper/2509.18471/) (2025)
- [A Power Law in Logarithm's Clothing: On the Scalability of Graph-Based Vector Search](https://mlsanity.com/paper/2609.02143/) (2026)

## 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.