FANNG: Fast Approximate Nearest Neighbour Graphs

By Ben Harwood, Tom Drummond. 2016

173 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Information Retrieval & Search, Algorithms & Complexity

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Citations in the corpus

Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2016, neutral): “A similar heuristicwas also a focus of the FANNG algorithm [47] (published shortly after the first versions of the current manuscript were posted online) with a slightly different interpretation, based on the sparse neighborhood graph’s property of the exact routing [18].”
Accelerating Large-Scale Inference with Anisotropic Vector Quantization (2019, background): “There is also a class of approaches based on graph search (Malkov & Yashunin, 2016; Harwood & Drummond, 2016).”
Smart Mining for Deep Metric Learning (2017, methodology): “a) Triplets mined from the CUB-200-2011 [23] training set using FANNG [5].”
A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search (2021, methodology): “Dozens of algorithms have been proposed to solve this problem from different optimizations [36, 37, 43, 54, 65, 67, 72].”
Low-Shot Learning with Large-Scale Diffusion (2017, methodology): “Several approximate algorithms [10, 23, 1, 17] have been proposed to efficiently produce the kNN graph used as input of iterative/diffusion methods, since this operation is of quadratic complexity in the number of images.”
Multiscale Quantization for Fast Similarity Search (2017, methodology): “Tree based methods [7, 29, 9], graph based methods [16] and locality sensitive hashing style algorithms [19, 1, 32] focus on non-exhaustive search by partitioning the search space.”