Nearest neighbor search

Nearest neighbor search is a long-standing problem arising in a large number of machine learning applications, such as recommender services, information retrieval, and others.

Area 11. Nearest Neighbor Search.svg

Posts

Publications

  • DeDrift: Robust Similarity Search under Content Drift

    Nearest neighbor search
    Dmitry Baranchuk
    Matthijs Douze
    Yash Upadhyay
    I. Zeki Yalniz
    ICCV, 2023

    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.

  • Graph-based Nearest Neighbor Search in Hyperbolic Spaces

    Machine learning theoryGraph machine learningNearest neighbor searchRepresentations
    Liudmila Prokhorenkova
    Dmitry Baranchuk
    Nikolay Bogachev
    Yury Demidovich
    Alexander Kolpakov
    ICLR, 2022

    The nearest neighbor search (NNS) problem is widely studied in Euclidean space, and graph-based algorithms are known to outperform other approaches for this task. However, hyperbolic geometry often allows for better data representation in various domains, including graphs, words, and images. In this paper, we show that graph-based approaches are also well suited for hyperbolic geometry. From a theoretical perspective, we rigorously analyze the time and space complexity of graph-based NNS, assuming that an n-element dataset is uniformly distributed within a d-dimensional ball of radius R in the hyperbolic space of curvature -1. Under some conditions on R and d, we derive the time and space complexity of graph-based NNS and compare the obtained results with known guarantees for the Euclidean case. Interestingly, in the dense setting (d << log(n)) and under some assumptions on the radius R, graph-based NNS has lower time complexity in the hyperbolic space. This agrees with our experiments: we consider datasets embedded in hyperbolic and Euclidean spaces and show that graph-based NNS can be more efficient in the hyperbolic space. We also demonstrate that graph-based methods outperform other existing baselines for hyperbolic NNS. Overall, our theoretical and empirical analysis suggests that graph-based NNS can be considered a default approach for similarity search in hyperbolic spaces.

  • Results of the NeurIPS’21 Challenge on Billion-Scale Approximate Nearest Neighbor Search

    Nearest neighbor search
    Harsha Vardhan Simhadri
    George Williams
    Martin Aumuller
    Matthijs Douze
    Artem Babenko
    Dmitry Baranchuk
    Qi Chen
    Lucas Hosseini
    Ravishankar Krishnaswamy
    Gopal Srinivasa
    Suhas Jayaram Subramanya
    Jingdong Wang
    NeurIPS Competitions, 2021

    Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets, typically of 1 million points. However, deploying recent advances in embedding based techniques for search, recommendation and ranking at scale require ANNS indices at billion, trillion or larger scale. Barring a few recent papers, there is limited consensus on which algorithms are effective at this scale vis-à-vis their hardware cost. This competition compares ANNS algorithms at billion-scale by hardware cost, accuracy and performance. We set up an open source evaluation framework and leaderboards for both standardized and specialized hardware. The competition involves three tracks. The standard hardware track T1 evaluates algorithms on an Azure VM with limited DRAM, often the bottleneck in serving billion-scale indices, where the embedding data can be hundreds of GigaBytes in size. It uses FAISS as the baseline. The standard hardware track T2 additional allows inexpensive SSDs in addition to the limited DRAM and uses DiskANN as the baseline. The specialized hardware track T3 allows any hardware configuration, and again uses FAISS as the baseline. We compiled six diverse billion-scale datasets, four newly released for this competition, that span a variety of modalities, data types, dimensions, deep learning models, distance functions and sources. The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold. Additionally, for track T3, separate leaderboards were created based on recall as well as cost-normalized and power-normalized query throughput.

Datasets

  • Text-to-Image dataset for billion-scale similarity search

    Computer visionNatural language processing Nearest neighbor search
    Dmitry Baranchuk
    Artem Babenko

    Yandex Text-to-Image (T2I) dataset is collected to foster the research in billion-scale nearest neighbor search (NNS) when query distribution differs from the indexing one. In particular, this dataset addresses the cross-domain setting: a user specifies a textual query and requests the search engine to retrieve the most relevant images to the query. Notably, current large-scale indexing methods perform poorly in this setting. Therefore, novel highly-performant indexing solutions robust to out-of-domain queries are in high demand.

    The dataset represents a snapshot of the Yandex visual search engine and contains 1 billion 200-dimensional image embeddings for indexing. The image embeddings are produced by the Se-ResNext-101 model. The embeddings for textual queries are extracted by a variant of the DSSM model.

    Read more about the data format and how to download the dataset in the related post.