Summary of Towards a General Framework For Improving the Performance Of Classifiers Using Xai Methods, by Andrea Apicella et al.
Towards a general framework for improving the performance of classifiers using XAI methods
by Andrea Apicella, Salvatore Giugliano, Francesco Isgrò, Roberto Prevete
First submitted to arxiv on: 15 Mar 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 The proposed framework uses Explainable Artificial Intelligence (XAI) methods to improve the performance of pre-trained Deep Learning (DL) classifiers without retraining complex models from scratch. The framework is based on two different learning strategies: auto-encoder-based and encoder-decoder-based, which are designed to avoid computational overhead. The study demonstrates the potential of XAI in automatically improving AI system performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers propose a new way to make artificial intelligence (AI) better by using special techniques called Explainable Artificial Intelligence (XAI). This helps understand how AI makes decisions. The idea is to use these techniques to improve the performance of AI systems without having to retrain them from scratch, which can be very time-consuming and expensive. The study shows two different ways to do this: auto-encoder-based and encoder-decoder-based. |
Keywords
* Artificial intelligence * Deep learning * Encoder * Encoder decoder
