# Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

arXiv: https://arxiv.org/abs/1504.03410 · HTML: https://mlsanity.com/paper/1504.03410/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2015.

## Built on by (most cited first, 30 of 44)

- [Efficient Passage Retrieval with Hashing for Open-domain Question Answering](https://mlsanity.com/paper/2106.00882/) (2021)
- [One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective](https://mlsanity.com/paper/2109.14449/) (2021)
- [Hash3D: Training-free Acceleration for 3D Generation](https://mlsanity.com/paper/2404.06091/) (2024)
- [Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning](https://mlsanity.com/paper/2109.08868/) (2021)
- [Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines](https://mlsanity.com/paper/2202.08679/) (2022)
- [ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization](https://mlsanity.com/paper/2203.03610/) (2022)
- [Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval](https://mlsanity.com/paper/2105.03647/) (2021)
- [Deep Hash Distillation for Image Retrieval](https://mlsanity.com/paper/2112.08816/) (2021)
- [Deep Triplet Hashing Network for Case-based Medical Image Retrieval](https://mlsanity.com/paper/2101.12346/) (2021)
- [TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval](https://mlsanity.com/paper/2105.01823/) (2021)
- [Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing](https://mlsanity.com/paper/2105.07553/) (2021)
- [Instance-weighted Central Similarity for Multi-label Image Retrieval](https://mlsanity.com/paper/2108.05274/) (2021)
- [Retrieving Black-box Optimal Images from External Databases](https://mlsanity.com/paper/2112.14921/) (2021)
- [HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation](https://mlsanity.com/paper/2505.16133/) (2025)
- [Rescuing Deep Hashing from Dead Bits Problem](https://mlsanity.com/paper/2102.00648/) (2021)
- [Deep Point Cloud Normal Estimation via Triplet Learning](https://mlsanity.com/paper/2110.10494/) (2021)
- [Neural networks behave as hash encoders: An empirical study](https://mlsanity.com/paper/2101.05490/) (2021)
- [Tensor Normalization and Full Distribution Training](https://mlsanity.com/paper/2109.02345/) (2021)
- [PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval](https://mlsanity.com/paper/2109.05206/) (2021)
- [HyP2 Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval](https://mlsanity.com/paper/2208.06866/) (2022)
- [DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection](https://mlsanity.com/paper/2310.06703/) (2023)
- [Deep Hashing with Semantic Hash Centers for Image Retrieval](https://mlsanity.com/paper/2507.08404/) (2025)
- [Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search](https://mlsanity.com/paper/2102.01486/) (2021)
- [Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval](https://mlsanity.com/paper/2103.09442/) (2021)
- [DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification](https://mlsanity.com/paper/2112.04937/) (2021)
- [CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval](https://mlsanity.com/paper/2204.10779/) (2022)
- [A Lower Bound of Hash Codes' Performance](https://mlsanity.com/paper/2210.05899/) (2022)
- [Data Dimension Reduction makes ML Algorithms efficient](https://mlsanity.com/paper/2211.09392/) (2022)
- [On the Stability and Generalization of Triplet Learning](https://mlsanity.com/paper/2302.09815/) (2023)
- [Learning Label Encodings for Deep Regression](https://mlsanity.com/paper/2303.02273/) (2023)

## Abstract (arXiv)

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.