AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes a saliency-driven distillation framework that constructs class-discriminative latent prototypes to enhance representativeness and generalization and demonstrates consistent performance improvements over strong baselines.
Activation-Weighted Adaptive REtention (AWARe) is a fine-tuning method that mitigates catastrophic forgetting by dynamically controlling parameter updates based on activation patterns, and operates without modifying model architectures, ensuring compatibility with existing inference engines.
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