# Collaborative Learning for Extremely Low Bit Asymmetric Hashing

arXiv: https://arxiv.org/abs/1809.09329 · HTML: https://mlsanity.com/paper/1809.09329/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Lightweight Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/2108.11333/) (2021)
- [Mitigating Generation Shifts for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2107.03163/) (2021)
- [Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization](https://mlsanity.com/paper/2110.12478/) (2021)
- [Attribute-aware Explainable Complementary Clothing Recommendation](https://mlsanity.com/paper/2107.01655/) (2021)
- [Nested Hash Layer: A Plug-and-play Module for Multiple-length Hash Code Learning](https://mlsanity.com/paper/2412.08922/) (2024)
- [Matryoshka Hash Representations for Model-Aware Compact Semantic Retrieval](https://mlsanity.com/paper/2609.07276/) (2026)
- [Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning](https://mlsanity.com/paper/2206.12592/) (2022)

## Abstract (arXiv)

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly guarantee a satisfactory performance with the extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss and the shrink of the discrete solution space. In this paper, we propose a novel textitCollaborative Learning strategy that is tailored for generating high-quality low-bit hash codes. The core idea is to jointly distill bit-specific and informative representations for a group of pre-defined code lengths. The learning of short hash codes among the group can benefit from the manifold shared with other long codes, where multiple views from different hash codes provide the supplementary guidance and regularization, making the convergence faster and more stable. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and have shown that the 8-bit hash codes generated by MAH achieve 94.3% of the MAP (Mean Average Precision (MAP)) score on the CIFAR-10 dataset, which significantly surpasses the performance of the 48-bit codes by the state-of-the-arts in image retrieval tasks.