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Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing: we investigate the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use. In addition, we propose a novel supervision approach in which the model uses its own predictions of the label distribution to implement the pairwise objective. Compared to the best baseline, this procedure yields similar performance in fully-supervised settings but improves significantly the results when labelled data is scarce.

License: Apache License 2.0

Python 74.76% Shell 25.24%
variational-autoencoder deep-learning neural-networks dimension-reduction binary-variational-autoencoder

ssb-vae's Introduction

Hi there, I am Antonio ๐Ÿ‘‹

Current Activity

๐ŸŽ“ I'm a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI) where I lead the Quantum Artificial Intelligence unit within the Intelligent Information Systems research team.

Interests

๐Ÿค” Things I'm curious about:

  • Quantum AI
  • Artificial Intelligence
  • Quantum Computing
  • Machine Learning

Contacts

๐Ÿ“ซ How to reach me: [email protected]

โ„น๏ธ More info:

Antonio Macaluso's LinkedIn Profile Antonio Macaluso's Google Scholar Profile Antonio Macaluso's Orcid Profile


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ssb-vae's Issues

Evaluating PSH model

Hi, Thanks for sharing implementation. I am working on text hashing and I tried to run the PSH code on 20 newsgroup dataset. In evaluation part this error occured: AxisError: axis 1 is out of bounds for array of dimension 0. It seems it happened because of different shape of vectors in this pairwise method. Could you please consider it and help me?

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