# Self-supervised Product Quantization for Deep Unsupervised Image Retrieval

arXiv: https://arxiv.org/abs/2109.02244 · HTML: https://mlsanity.com/paper/2109.02244/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [CURL: Contrastive Unsupervised Representations for Reinforcement Learning](https://mlsanity.com/paper/2004.04136/) (2020)
- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https://mlsanity.com/paper/1910.02190/) (2019)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Central Similarity Quantization for Efficient Image and Video Retrieval](https://mlsanity.com/paper/1908.00347/) (2019)
- [On Sampling Strategies for Neural Network-based Collaborative Filtering](https://mlsanity.com/paper/1706.07881/) (2017)
- [Deep Supervised Discrete Hashing](https://mlsanity.com/paper/1705.10999/) (2017)
- [DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs](https://mlsanity.com/paper/1905.03465/) (2019)
- [Auto-Encoding Twin-Bottleneck Hashing](https://mlsanity.com/paper/2002.11930/) (2020)
- [Binary Generative Adversarial Networks for Image Retrieval](https://mlsanity.com/paper/1708.04150/) (2017)
- [Generalized Product Quantization Network for Semi-supervised Image Retrieval](https://mlsanity.com/paper/2002.11281/) (2020)
- [Stochastic Generative Hashing](https://mlsanity.com/paper/1701.02815/) (2017)
- [End-to-End Supervised Product Quantization for Image Search and Retrieval](https://mlsanity.com/paper/1711.08589/) (2017)
- [Deep Triplet Quantization](https://mlsanity.com/paper/1902.00153/) (2019)
- [Unsupervised Neural Quantization for Compressed-Domain Similarity Search](https://mlsanity.com/paper/1908.03883/) (2019)
- [BinGAN: Learning Compact Binary Descriptors with a Regularized GAN](https://mlsanity.com/paper/1806.06778/) (2018)
- [SUBIC: A supervised, structured binary code for image search](https://mlsanity.com/paper/1708.02932/) (2017)

## Built on by (most cited first, 28 of 28)

- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding](https://mlsanity.com/paper/2406.08877/) (2024)
- [Residual Quantization with Implicit Neural Codebooks](https://mlsanity.com/paper/2401.14732/) (2024)
- [IRGen: Generative Modeling for Image Retrieval](https://mlsanity.com/paper/2303.10126/) (2023)
- [Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems](https://mlsanity.com/paper/2303.01669/) (2023)
- [Deep Hash Distillation for Image Retrieval](https://mlsanity.com/paper/2112.08816/) (2021)
- [Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey](https://mlsanity.com/paper/2507.22920/) (2025)
- [EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing](https://mlsanity.com/paper/2208.14657/) (2022)
- [ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents](https://mlsanity.com/paper/2303.03127/) (2023)
- [Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization](https://mlsanity.com/paper/2312.07342/) (2023)
- [Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval](https://mlsanity.com/paper/2206.08842/) (2022)
- [CUCL: Codebook for Unsupervised Continual Learning](https://mlsanity.com/paper/2311.14911/) (2023)
- [On Learning Discriminative Features from Synthesized Data for Self-Supervised Fine-Grained Visual Recognition](https://mlsanity.com/paper/2407.14676/) (2024)
- [Image Hashing via Cross-View Code Alignment in the Age of Foundation Models](https://mlsanity.com/paper/2510.27584/) (2025)
- [Weighted Contrastive Hashing](https://mlsanity.com/paper/2209.14099/) (2022)
- [Set2Box: Similarity Preserving Representation Learning of Sets](https://mlsanity.com/paper/2210.03282/) (2022)
- [DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes](https://mlsanity.com/paper/2305.18441/) (2023)
- [Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol](https://mlsanity.com/paper/2507.06070/) (2025)
- [From Image Hashing to Scene Change Detection](https://mlsanity.com/paper/2605.12259/) (2026)
- [Collision-Resistant Single-Pass Method for Unsupervised Fine-Grained Image Hashing](https://mlsanity.com/paper/2605.18288/) (2026)
- [Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval](https://mlsanity.com/paper/2206.09806/) (2022)
- [Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval](https://mlsanity.com/paper/2401.15362/) (2024)
- [Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing](https://mlsanity.com/paper/2403.06071/) (2024)
- [Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning](https://mlsanity.com/paper/2403.07231/) (2024)
- [Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval](https://mlsanity.com/paper/2405.14726/) (2024)
- [Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap](https://mlsanity.com/paper/2501.14826/) (2025)
- [Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction](https://mlsanity.com/paper/2510.08449/) (2025)
- [LooC: Effective Low-Dimensional Codebook for Compositional Vector Quantization](https://mlsanity.com/paper/2601.00222/) (2026)

## Abstract (arXiv)

Supervised deep learning-based hash and vector quantization are enabling fast and large-scale image retrieval systems. By fully exploiting label annotations, they are achieving outstanding retrieval performances compared to the conventional methods. However, it is painstaking to assign labels precisely for a vast amount of training data, and also, the annotation process is error-prone. To tackle these issues, we propose the first deep unsupervised image retrieval method dubbed Self-supervised Product Quantization (SPQ) network, which is label-free and trained in a self-supervised manner. We design a Cross Quantized Contrastive learning strategy that jointly learns codewords and deep visual descriptors by comparing individually transformed images (views). Our method analyzes the image contents to extract descriptive features, allowing us to understand image representations for accurate retrieval. By conducting extensive experiments on benchmarks, we demonstrate that the proposed method yields state-of-the-art results even without supervised pretraining.