# Unsupervised Neural Quantization for Compressed-Domain Similarity Search

arXiv: https://arxiv.org/abs/1908.03883 · HTML: https://mlsanity.com/paper/1908.03883/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction](https://mlsanity.com/paper/2305.11421/) (2023)
- [Self-supervised Product Quantization for Deep Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.02244/) (2021)
- [Distribution-sensitive Information Retention for Accurate Binary Neural Network](https://mlsanity.com/paper/2109.12338/) (2021)
- [Residual Quantization with Implicit Neural Codebooks](https://mlsanity.com/paper/2401.14732/) (2024)
- [Contrastive Quantization with Code Memory for Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.05205/) (2021)
- [OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries](https://mlsanity.com/paper/2211.12850/) (2022)
- [Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks](https://mlsanity.com/paper/2501.03078/) (2025)
- [Semi-supervised Network Embedding with Differentiable Deep Quantisation](https://mlsanity.com/paper/2108.09128/) (2021)
- [FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2206.11408/) (2022)
- [RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression](https://mlsanity.com/paper/2605.14359/) (2026)
- [Set2Box: Similarity Preserving Representation Learning of Sets](https://mlsanity.com/paper/2210.03282/) (2022)
- [Lossless Compression of Vector IDs for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2501.10479/) (2025)
- [Machine learning and high dimensional vector search](https://mlsanity.com/paper/2502.16931/) (2025)
- [Cluster with Auctions for Vector Search](https://mlsanity.com/paper/2607.13728/) (2026)

## Abstract (arXiv)

We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems. While the deep learning machinery has benefited literally all computer vision pipelines, the existing state-of-the-art compression methods employ shallow architectures, and we aim to close this gap by our paper. In more detail, we introduce a DNN architecture for the unsupervised compressed-domain retrieval, based on multi-codebook quantization. The proposed architecture is designed to incorporate both fast data encoding and efficient distances computation via lookup tables. We demonstrate the exceptional advantage of our scheme over existing quantization approaches on several datasets of visual descriptors via outperforming the previous state-of-the-art by a large margin.