# MIHash: Online Hashing with Mutual Information

arXiv: https://arxiv.org/abs/1703.08919 · HTML: https://mlsanity.com/paper/1703.08919/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2017.

## Built on by (most cited first, 8 of 8)

- [Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring](https://mlsanity.com/paper/2206.06818/) (2022)
- [Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian](https://mlsanity.com/paper/2405.13929/) (2024)
- [Locally-Adaptive Quantization for Streaming Vector Search](https://mlsanity.com/paper/2402.02044/) (2024)
- [Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing](https://mlsanity.com/paper/2112.06489/) (2021)
- [Unsupervised Hashing with Similarity Distribution Calibration](https://mlsanity.com/paper/2302.07669/) (2023)
- [Online Hashing with Similarity Learning](https://mlsanity.com/paper/2108.02560/) (2021)
- [Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data](https://mlsanity.com/paper/2109.04260/) (2021)
- [Fast Online Hashing with Multi-Label Projection](https://mlsanity.com/paper/2212.03112/) (2022)

## Abstract (arXiv)

Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data. In this paper, we first address a key challenge for online hashing: the binary codes for indexed data must be recomputed to keep pace with updates to the hash functions. We propose an efficient quality measure for hash functions, based on an information-theoretic quantity, mutual information, and use it successfully as a criterion to eliminate unnecessary hash table updates. Next, we also show how to optimize the mutual information objective using stochastic gradient descent. We thus develop a novel hashing method, MIHash, that can be used in both online and batch settings. Experiments on image retrieval benchmarks (including a 2.5M image dataset) confirm the effectiveness of our formulation, both in reducing hash table recomputations and in learning high-quality hash functions.