On-the-go Forgetting without Explicit Unlearning via ERASE
This work introduces ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forgetting that suppresses the observable influence of private data without modifying model weights, establishing a scalable, regulation-aligned pathway for continual, privacy-conscious learning.