# Stochastic Generative Hashing

arXiv: https://arxiv.org/abs/1701.02815 · HTML: https://mlsanity.com/paper/1701.02815/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2017.

## Built on by (most cited first, 17 of 17)

- [Self-supervised Product Quantization for Deep Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.02244/) (2021)
- [Unsupervised Hashing with Contrastive Information Bottleneck](https://mlsanity.com/paper/2105.06138/) (2021)
- [Contrastive Quantization with Code Memory for Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.05205/) (2021)
- [Hyperbolic Hierarchical Contrastive Hashing](https://mlsanity.com/paper/2212.08904/) (2022)
- [NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval](https://mlsanity.com/paper/2404.11025/) (2024)
- [Learning to Hash Naturally Sorts](https://mlsanity.com/paper/2201.13322/) (2022)
- [Deep Self-Adaptive Hashing for Image Retrieval](https://mlsanity.com/paper/2108.07094/) (2021)
- [A Lower Bound of Hash Codes' Performance](https://mlsanity.com/paper/2210.05899/) (2022)
- [Unsupervised Hashing with Similarity Distribution Calibration](https://mlsanity.com/paper/2302.07669/) (2023)
- [Weighted Contrastive Hashing](https://mlsanity.com/paper/2209.14099/) (2022)
- [Collision-Resistant Single-Pass Method for Unsupervised Fine-Grained Image Hashing](https://mlsanity.com/paper/2605.18288/) (2026)
- [Deep Unsupervised Hashing by Distilled Smooth Guidance](https://mlsanity.com/paper/2105.06125/) (2021)
- [Deep Unsupervised Hashing with Latent Semantic Components](https://mlsanity.com/paper/2203.09420/) (2022)
- [Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval](https://mlsanity.com/paper/2206.09806/) (2022)
- [Sparse-Inductive Generative Adversarial Hashing for Nearest Neighbor Search](https://mlsanity.com/paper/2306.06928/) (2023)
- [Supervised Auto-Encoding Twin-Bottleneck Hashing](https://mlsanity.com/paper/2306.11122/) (2023)
- [Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing](https://mlsanity.com/paper/2403.06071/) (2024)

## Abstract (arXiv)

Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases. However, due to the requirement of discrete outputs for the hash functions, learning such functions is known to be very challenging. In addition, the objective functions adopted by existing hashing techniques are mostly chosen heuristically. In this paper, we propose a novel generative approach to learn hash functions through Minimum Description Length principle such that the learned hash codes maximally compress the dataset and can also be used to regenerate the inputs. We also develop an efficient learning algorithm based on the stochastic distributional gradient, which avoids the notorious difficulty caused by binary output constraints, to jointly optimize the parameters of the hash function and the associated generative model. Extensive experiments on a variety of large-scale datasets show that the proposed method achieves better retrieval results than the existing state-of-the-art methods.