Class-Incremental Learning with Zero-Shot Unlearning via Activation Disentanglement

We study class-incremental learning (CIL) with zero-shot unlearning, where previously learned task-specific knowledge must be removed without access to past data. Existing CIL methods reduce forgetting during sequential learning, but they do not ensure safe unlearning: deleting a task-specific component can still perturb later-task activations and degrade retained performance. Motivated by this activation-space entanglement challenge, we introduce Anti-Entanglement of Activation (AEA), which enforces inter-task orthogonality in representation space to enable unlearning by parameter deletion. Building on this idea, we propose CAD, a buffer-free class-incremental learning framework with dual-branch LoRA adapters that separate shared and removable knowledge, allowing zero-shot unlearning through simple adapter detachment rather than retraining. The framework further facilitates unlearning by selectively deleting forgotten-task basis vectors from gradient-projection memory. Across multiple benchmarks, CAD achieves stronger retention–removal trade-offs and lower membership-inference risk than existing baselines. Our code is available https://anonymous.4open.science/r/DaCIL-115D/here.