Deep Cross Modal Hashing Implementation in PyTorch

This repository provides a PyTorch implementation for deep learning cross-modal hashing. It was authored by WangGodder and last updated in October 2021. The specific dataset details, including row and column counts, are unknown.

Use Cases

• Implement cross-modal retrieval models using the provided PyTorch codebase for data visualization tasks.
• Study and extend the deep learning architecture for generating hash codes across different data modalities.
• Use the framework to experiment with Cross Modal Hashing techniques on custom datasets.

Strengths

• The code is released under the permissive MIT license.
• The project was actively maintained, with a last update recorded on October 7, 2021.

Limitations

• The repository is a codebase, not a dataset; no data rows, columns, or sample data are provided.
• The implementation's performance and applicability depend entirely on the user providing their own data.

Provenance

Source

GitHub repository by author WangGodder.

Collection Method

Code implementation for a machine learning algorithm.

Freshness

Last updated 2021-10-07.