Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.
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Updated
Apr 14, 2019 - Python
Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.
Pure python implementation of product quantization for nearest neighbor search
⚡ A fast embedded library for approximate nearest neighbor search
WSDM'22 Best Paper: Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
Self-supervised Product Quantization for Deep Unsupervised Image Retrieval - ICCV2021
Generalized Product Quantization Network For Semi-supervised Image Retrieval - CVPR 2020
Implementation of vector quantization algorithms, codes for Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search.
CIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.
A tiny approximate K-Nearest Neighbour library in Python based on Fast Product Quantization and IVF
[DEPRECATED] Baseline Project for Semantic Searching
Scene similarity for weak object discovery & classification
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
basis embedding: a product quantization based model compression method for language models.
This repo includes a Sparse Transformer implementation which utilizes PQ to derive the sparsity.
Transformer-based Embedding Retrieval with Product Quantization for Edge Computing (JavaScript)
Converted version of yahoo LOPQ from python2.7 to python3.6
Implementations of different NLP tasks
Pytorch implementation of LISA (Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation. WWW 2021)
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