# Hashing with Mutual Information

arXiv: https://arxiv.org/abs/1803.00974 · HTML: https://mlsanity.com/paper/1803.00974/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring](https://mlsanity.com/paper/2206.06818/) (2022)
- [TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval](https://mlsanity.com/paper/2105.01823/) (2021)
- [Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.12894/) (2023)
- [BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities](https://mlsanity.com/paper/2410.14672/) (2024)
- [Ternary Hashing](https://mlsanity.com/paper/2103.09173/) (2021)
- [DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification](https://mlsanity.com/paper/2112.04937/) (2021)
- [Unsupervised Hashing with Similarity Distribution Calibration](https://mlsanity.com/paper/2302.07669/) (2023)
- [Nested Hash Layer: A Plug-and-play Module for Multiple-length Hash Code Learning](https://mlsanity.com/paper/2412.08922/) (2024)
- [GLRT-Based Metric Learning for Remote Sensing Object Retrieval](https://mlsanity.com/paper/2410.05773/) (2024)

## Abstract (arXiv)

Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval. We study the problem of learning binary vector embeddings under a supervised setting, also known as hashing. We propose a novel supervised hashing method based on optimizing an information-theoretic quantity: mutual information. We show that optimizing mutual information can reduce ambiguity in the induced neighborhood structure in the learned Hamming space, which is essential in obtaining high retrieval performance. To this end, we optimize mutual information in deep neural networks with minibatch stochastic gradient descent, with a formulation that maximally and efficiently utilizes available supervision. Experiments on four image retrieval benchmarks, including ImageNet, confirm the effectiveness of our method in learning high-quality binary embeddings for nearest neighbor retrieval.