# The Power of Asymmetry in Binary Hashing

arXiv: https://arxiv.org/abs/1311.7662 · HTML: https://mlsanity.com/paper/1311.7662/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2013.

## Built on by (most cited first, 3 of 3)

- [Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization](https://mlsanity.com/paper/2110.12478/) (2021)
- [Online Hashing with Similarity Learning](https://mlsanity.com/paper/2108.02560/) (2021)
- [Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning](https://mlsanity.com/paper/2206.12592/) (2022)

## Abstract (arXiv)

When approximating binary similarity using the hamming distance between short binary hashes, we show that even if the similarity is symmetric, we can have shorter and more accurate hashes by using two distinct code maps. I.e. by approximating the similarity between x and x' as the hamming distance between f(x) and g(x'), for two distinct binary codes f,g, rather than as the hamming distance between f(x) and f(x').