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Explanation-Guided Adversarial Training for Robust and Interpretable Models
arXiv – CS AI|Chao Chen, Yanhui Chen, Shanshan Lin, Dongsheng Hong, Shu Wu, Xiangwen Liao, Chuanyi Liu||3 views
🤖AI Summary
Researchers propose Explanation-Guided Adversarial Training (EGAT), a framework that combines adversarial training with explainable AI to create more robust and interpretable deep neural networks. The method achieves 37% improvement in adversarial accuracy while producing semantically meaningful explanations with only 16% increase in training time.
Key Takeaways
- →EGAT integrates adversarial training with explanation-guided learning to improve both robustness and interpretability of neural networks.
- →The framework generates adversarial examples while imposing explanation-based constraints during training.
- →EGAT demonstrates 37% improvement in adversarial accuracy compared to competitive baselines.
- →The method produces more semantically meaningful explanations while requiring only 16% additional training time.
- →Theoretical analysis shows EGAT yields more stable predictions under unexpected situations compared to standard adversarial training.
#adversarial-training#explainable-ai#neural-networks#machine-learning#robustness#interpretability#deep-learning#ai-safety
Read Original →via arXiv – CS AI
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