# Deep Unsupervised Image Hashing by Maximizing Bit Entropy

arXiv: https://arxiv.org/abs/2012.12334 · HTML: https://mlsanity.com/paper/2012.12334/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 20 of 20)

- [One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective](https://mlsanity.com/paper/2109.14449/) (2021)
- [Contrastive Quantization with Code Memory for Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.05205/) (2021)
- [COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing](https://mlsanity.com/paper/2402.16424/) (2024)
- [Equal Bits: Enforcing Equally Distributed Binary Network Weights](https://mlsanity.com/paper/2112.03406/) (2021)
- [Deep Hash Distillation for Image Retrieval](https://mlsanity.com/paper/2112.08816/) (2021)
- [Hyperbolic Hierarchical Contrastive Hashing](https://mlsanity.com/paper/2212.08904/) (2022)
- [Learning to Hash Naturally Sorts](https://mlsanity.com/paper/2201.13322/) (2022)
- [CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing](https://mlsanity.com/paper/2310.18926/) (2023)
- [Unsupervised Hashing with Similarity Distribution Calibration](https://mlsanity.com/paper/2302.07669/) (2023)
- [Image Hashing via Cross-View Code Alignment in the Age of Foundation Models](https://mlsanity.com/paper/2510.27584/) (2025)
- [Self-Supervised Learning as Discrete Communication](https://mlsanity.com/paper/2602.09764/) (2026)
- [Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings](https://mlsanity.com/paper/2609.03376/) (2026)
- [Weighted Contrastive Hashing](https://mlsanity.com/paper/2209.14099/) (2022)
- [ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery](https://mlsanity.com/paper/2406.08457/) (2024)
- [From Image Hashing to Scene Change Detection](https://mlsanity.com/paper/2605.12259/) (2026)
- [Collision-Resistant Single-Pass Method for Unsupervised Fine-Grained Image Hashing](https://mlsanity.com/paper/2605.18288/) (2026)
- [Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval](https://mlsanity.com/paper/2206.09806/) (2022)
- [Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval](https://mlsanity.com/paper/2401.15362/) (2024)
- [Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing](https://mlsanity.com/paper/2403.06071/) (2024)
- [Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing](https://mlsanity.com/paper/2503.21236/) (2025)

## Abstract (arXiv)

Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes for compact storage and efficient semantic retrieval. We propose an unsupervised deep hashing layer called Bi-half Net that maximizes entropy of the binary codes. Entropy is maximal when both possible values of the bit are uniformly (half-half) distributed. To maximize bit entropy, we do not add a term to the loss function as this is difficult to optimize and tune. Instead, we design a new parameter-free network layer to explicitly force continuous image features to approximate the optimal half-half bit distribution. This layer is shown to minimize a penalized term of the Wasserstein distance between the learned continuous image features and the optimal half-half bit distribution. Experimental results on the image datasets Flickr25k, Nus-wide, Cifar-10, Mscoco, Mnist and the video datasets Ucf-101 and Hmdb-51 show that our approach leads to compact codes and compares favorably to the current state-of-the-art.