Label-agnostic, representation-level machine unlearning via feature disentanglement

• We propose a label-agnostic machine unlearning framework that does not require any class supervision during the unlearning phase, addressing practical and privacy-driven limitations of existing supervised MU methods. • We demonstrate that our method achieves effective unlearning by removing only the target-specific information while preserving the performance on retain data. • We validate the proposed method across various unlearning scenarios (e.g., class unlearning, sub-class unlearning, random sample unlearning, noisy label entry unlearning) and show that it outperforms existing baselines in terms of both privacy robustness and utility retention.