Summary of Contrastive Learning and Adversarial Disentanglement For Privacy-preserving Task-oriented Semantic Communications, by Omar Erak et al.
Contrastive Learning and Adversarial Disentanglement for Privacy-Preserving Task-Oriented Semantic Communications
by Omar Erak, Omar Alhussein, Wen Tong
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); 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 information-bottleneck method, called CLAD (contrastive learning and adversarial disentanglement), aims to efficiently transmit data while communicating only task-relevant information. By leveraging contrastive learning to capture relevant features and employing adversarial disentanglement to discard irrelevant information, CLAD outperforms state-of-the-art baselines in terms of task performance, privacy preservation, and informativeness. The method also introduces a new technique to compute the information retention index (IRI), which quantifies the minimality and informativeness of encoded feature vectors across different communication techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CLAD is a new way to share information that only includes what’s important for a specific task. Right now, there are problems with sharing too much unnecessary information, which can be bad for privacy. To fix this, CLAD uses two parts: one that finds the important features and another that gets rid of the unimportant ones. This helps make sure that only relevant information is shared. The paper also introduces a new way to measure how good this method is at sharing information. |
