Summary of Autoencoder-based Domain Learning For Semantic Communication with Conceptual Spaces, by Dylan Wheeler and Balasubramaniam Natarajan
Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces
by Dylan Wheeler, Balasubramaniam Natarajan
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Image and Video Processing (eess.IV)
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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 The proposed work aims to develop a framework for learning a domain of a conceptual space model using only raw data with high-level property labels. This is a crucial step in achieving accurate semantic communication, which has gained significant attention in recent years. By leveraging modern AI and ML techniques, the authors aim to improve the efficiency and robustness of communication systems while explicitly modeling meaning in a geometric manner. The framework is tested on the MNIST and CelebA datasets, showing promising results with domains that maintain semantic similarity relations and possess interpretable dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to understand what people mean when they communicate. Usually, we just try to get the right words across, but this approach doesn’t always work. The authors are trying to change that by using special math tools called conceptual spaces to capture the meaning behind messages. They’ve been working on this idea for a while and have made some progress, but there’s still a lot of work to be done. In this paper, they’re sharing their latest ideas and results from testing them on pictures of handwritten numbers and celebrity faces. |
Keywords
* Artificial intelligence * Attention
