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The document presents a novel approach called Contrastive Adversarial Knowledge Distillation (CAKD) aimed at compressing deep learning models for time-series regression tasks, specifically in predicting Remaining Useful Life (RUL). CAKD addresses limitations of traditional knowledge distillation methods by integrating adversarial and contrastive learning to align feature distributions between teacher and student models with different architectures. Experimental results demonstrate that CAKD outperforms exis
- Author
- Chureendar T
- Language
- EN