# DeepHash: Getting Regularization, Depth and Fine-Tuning Right

arXiv: https://arxiv.org/abs/1501.04711 · HTML: https://mlsanity.com/paper/1501.04711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2015.

## Built on by (most cited first, 2 of 2)

- [Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation](https://mlsanity.com/paper/2307.15514/) (2023)
- [PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration](https://mlsanity.com/paper/2209.00219/) (2022)

## Abstract (arXiv)

This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to making DeepHash work at extremely low bitrates are three important considerations -- regularization, depth and fine-tuning -- each requiring solutions specific to the hashing problem. In-depth evaluation shows that our scheme consistently outperforms state-of-the-art methods across all data sets for both Fisher Vectors and Deep Convolutional Neural Network features, by up to 20 percent over other schemes. The retrieval performance with 256-bit hashes is close to that of the uncompressed floating point features -- a remarkable 512 times compression.