Queries Processing

Similarity Search - Faiss

The inverted file from “Video google: A text retrieval approach to object matching in videos.”, Sivic & Zisserman, ICCV 2003. This is the key to non-exhaustive search in large datasets. Otherwise all searches would need to scan all elements in the index, which is prohibitive even if the operation to apply for each element is fast
The product quantization (PQ) method from “Product quantization for nearest neighbor search”, Jégou & al., PAMI 2011. This can be seen as a lossy compression technique for high-dimensional vectors, that allows relatively accurate reconstructions and distance computations in the compressed domain.
The three-level quantization (IVFADC-R aka IndexIVFPQR) method from "Searching in one billion vectors: re-rank with source coding", Tavenard & al., ICASSP'11.
The inverted multi-index from “The inverted multi-index”, Babenko & Lempitsky, CVPR 2012. This method greatly improves the speed of inverted indexing for fast/less accurate operating points.
The optimized PQ from “Optimized product quantization”, He & al, CVPR 2013. This method can be seen as a linear transformation of the vector space to make it more amenable for indexing with a product quantizer.
The pre-filtering of product quantizer distances from “Polysemous codes”, Douze & al., ECCV 2016. This technique performs a binary filtering stage before computing PQ distances.
The GPU implementation and fast k-selection is described in “Billion-scale similarity search with GPUs”, Johnson & al, ArXiv 1702.08734, 2017
The HNSW indexing method from "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs", Malkov & al., ArXiv 1603.09320, 2016
The in-register vector comparisons from "Quicker ADC : Unlocking the Hidden Potential of Product Quantization with SIMD", André et al, PAMI'19, also used in "Accelerating Large-Scale Inference with Anisotropic Vector Quantization", Guo, Sun et al, ICML'20.
A general paper about product quantization and related methods: "A Survey of Product Quantization", Yusuke Matsui, Yusuke Uchida, Hervé Jégou, Shin’ichi Satoh, ITE transactions on MTA, 2018.

Knowledge Graph

KGVec2go - Knowledge Graph Embeddings as a Service

PreviousDisk ANN NextBuilding An Elastic Query Engine on Disaggregated Storage

Last updated 4 years ago

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Queries Processing

Similarity Search - Faiss

The inverted file from “Video google: A text retrieval approach to object matching in videos.”, Sivic & Zisserman, ICCV 2003. This is the key to non-exhaustive search in large datasets. Otherwise all searches would need to scan all elements in the index, which is prohibitive even if the operation to apply for each element is fast
The product quantization (PQ) method from “Product quantization for nearest neighbor search”, Jégou & al., PAMI 2011. This can be seen as a lossy compression technique for high-dimensional vectors, that allows relatively accurate reconstructions and distance computations in the compressed domain.
The three-level quantization (IVFADC-R aka IndexIVFPQR) method from "Searching in one billion vectors: re-rank with source coding", Tavenard & al., ICASSP'11.
The inverted multi-index from “The inverted multi-index”, Babenko & Lempitsky, CVPR 2012. This method greatly improves the speed of inverted indexing for fast/less accurate operating points.
The optimized PQ from “Optimized product quantization”, He & al, CVPR 2013. This method can be seen as a linear transformation of the vector space to make it more amenable for indexing with a product quantizer.
The pre-filtering of product quantizer distances from “Polysemous codes”, Douze & al., ECCV 2016. This technique performs a binary filtering stage before computing PQ distances.
The GPU implementation and fast k-selection is described in “Billion-scale similarity search with GPUs”, Johnson & al, ArXiv 1702.08734, 2017
The HNSW indexing method from "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs", Malkov & al., ArXiv 1603.09320, 2016
The in-register vector comparisons from "Quicker ADC : Unlocking the Hidden Potential of Product Quantization with SIMD", André et al, PAMI'19, also used in "Accelerating Large-Scale Inference with Anisotropic Vector Quantization", Guo, Sun et al, ICML'20.
A general paper about product quantization and related methods: "A Survey of Product Quantization", Yusuke Matsui, Yusuke Uchida, Hervé Jégou, Shin’ichi Satoh, ITE transactions on MTA, 2018.

Knowledge Graph

KGVec2go - Knowledge Graph Embeddings as a Service

PreviousDisk ANN NextBuilding An Elastic Query Engine on Disaggregated Storage

Last updated 4 years ago

Was this helpful?