Summary of Rank Reduction Autoencoders, by Jad Mounayer et al.
Rank Reduction Autoencoders
by Jad Mounayer, Chady Ghnatios, Sebastian Rodriguez, Mohammed El Fallaki Idrissi, Charbel Farhat, Francisco Chinesta
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors propose a new class of deterministic autoencoders called Rank Reduction Autoencoders (RRAEs) that employ truncated Singular Value Decomposition (SVD) to regularize their latent spaces. This approach enables the definition of the bottleneck dimension by the rank of the latent matrix, reducing dependence on the architecture’s size. The authors also introduce an adaptive algorithm (aRRAEs) that determines the optimal bottleneck size during training. Experiments on synthetic and benchmark datasets (MNIST, Fashion MNIST, CelebA) show that RRAEs outperform Vanilla AEs with both large and small latent spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new type of autoencoder called Rank Reduction Autoencoders. They use a special technique to make the data more organized and easy to understand. This helps them find the right amount of information to keep from the original data. The authors also create an algorithm that can figure out how much information is needed automatically. They test their idea on some standard datasets and show that it works better than other similar ideas. |
Keywords
» Artificial intelligence » Autoencoder
