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<h5>Description:</h5>
<p>Repository for reproducing results of our paper &ldquo;Deep image hashing based on twin-bottleneck hashing with variational autoencoders&rdquo; (Verwilst 2021). It contains the code for training the twin-bottleneck variational autoencoder architecture, evaluating the learned hash function, and performing content-based image retrieval using the learned hash function.</p>
<p>&nbsp;</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s): </strong>GC1 - WP4</li>
<li><strong>Responsible Research Lead:</strong> <a href="mailto:Aleksandra.Pizurica@UGent.be" target="_blank" rel="noopener">Aleksandra Pizurica</a></li>
<li><strong>Authors:</strong> <a href="mailto:maxim.verwilst@gmail.com" target="_blank" rel="noopener">Maxim Verwilst</a>, <a href="mailto:nina.zizakic@ugent.be" target="_blank" rel="noopener">Nina Zizakic</a>, <a href="mailto:lingchengu@mail.sdu.edu.cn" target="_blank" rel="noopener">Lingchen Gu</a></li>
<li><strong>Programming Language: </strong>Python</li>
<li><a href="https://github.com/maximverwilst/deepimagehashing-VAE" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories:</strong> Un/Semi-supervised learning, Images</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>M. Verwilst, N. Zizakic, L. Gu, A. Pizurica, &ldquo;<a href="https://biblio.ugent.be/publication/8716990" target="_blank" rel="noopener">Deep image hashing based on twin-bottleneck hashing with variational autoencoders</a>&rdquo;, IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP), 2021.</li>
</ul>

<p>Code for deep image hashing based on twin-bottleneck architecture with variational autoencoders.</p>