Summary of Semantic Meta-split Learning: a Tinyml Scheme For Few-shot Wireless Image Classification, by Eslam Eldeeb et al.
Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
by Eslam Eldeeb, Mohammad Shehab, Hirley Alves, Mohamed-Slim Alouini
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 TinyML-based semantic communication framework for few-shot wireless image classification integrates split-learning and meta-learning to overcome challenges in semantic and goal-oriented (SGO) communication. The framework limits computations performed by end-users while ensuring privacy-preserving using split-learning, and speeds up training with meta-learning. The algorithm is tested on a dataset of images of hand-written letters, achieving 20% gain in classification accuracy using fewer data points while consuming less training energy. Conformal prediction (CP) techniques are also used to analyze uncertainty in predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for devices to send only the most important information needed to complete a specific task. This helps with challenges like too much computation, not enough data, and keeping information private. The solution uses two techniques: split-learning to keep computations simple and meta-learning to learn quickly from similar tasks. The idea is tested on images of handwritten letters and shows that it’s 20% better at classifying things using less energy. |
Keywords
» Artificial intelligence » Classification » Few shot » Image classification » Meta learning
