AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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PAVA pairs a forget loss with a visual-attribute anchor that preserves image-grounded behavior by distilling the model's own pre-unlearning answers from the forget images alone and gives the strongest forget-retain trade-off among forget-set-only methods and remains competitive with retain-based baselines.
AIM is proposed, a two-stage method that anchors an identity-forgetting target with a universal visual prompt and then matches the vision encoder to that target under a Fisher-based constraint, which achieves competitive identity forgetting while preserving non-deleted identities, prior knowledge, and visual perception on the same images.
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