# End-to-End Supervised Product Quantization for Image Search and Retrieval

arXiv: https://arxiv.org/abs/1711.08589 · HTML: https://mlsanity.com/paper/1711.08589/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 16 of 16)

- [Self-supervised Product Quantization for Deep Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.02244/) (2021)
- [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)
- [Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey](https://mlsanity.com/paper/2507.22920/) (2025)
- [PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration](https://mlsanity.com/paper/2305.18334/) (2023)
- [Matching-oriented Product Quantization For Ad-hoc Retrieval](https://mlsanity.com/paper/2104.07858/) (2021)
- [Composite Code Sparse Autoencoders for first stage retrieval](https://mlsanity.com/paper/2204.07023/) (2022)
- [Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization](https://mlsanity.com/paper/2312.07342/) (2023)
- [Orthonormal Product Quantization Network for Scalable Face Image Retrieval](https://mlsanity.com/paper/2107.00327/) (2021)
- [Set2Box: Similarity Preserving Representation Learning of Sets](https://mlsanity.com/paper/2210.03282/) (2022)
- [Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval](https://mlsanity.com/paper/2403.05690/) (2024)
- [Open-Set Representation Learning through Combinatorial Embedding](https://mlsanity.com/paper/2106.15278/) (2021)
- [Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging](https://mlsanity.com/paper/2204.01979/) (2022)
- [Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval](https://mlsanity.com/paper/2206.09806/) (2022)
- [Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval](https://mlsanity.com/paper/2405.14726/) (2024)
- [Model Inversion Attack Against Deep Hashing](https://mlsanity.com/paper/2511.12233/) (2025)

## Abstract (arXiv)

Product Quantization, a dictionary based hashing method, is one of the leading unsupervised hashing techniques. While it ignores the labels, it harnesses the features to construct look up tables that can approximate the feature space. In recent years, several works have achieved state of the art results on hashing benchmarks by learning binary representations in a supervised manner. This work presents Deep Product Quantization (DPQ), a technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a dictionary-based representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straight-through estimator. Our method obtains state of the art results on an extensive array of retrieval and classification experiments.