Parameter-Efficien Data-Free Untraining in Test-Time Adaptation

In real-world applications, online adaptation of a source-trained model to contin- ually changing target domains is becoming increasingly common and important. This process is known as test-time adaptation (TTA) and is used in applications such as driving and aerial perception. This inevitably raises data-related risks: continually arriving data may contain corrupted samples or contaminated informa- tion, which can impair model reliability, as well as sensitive information, which can raise privacy risks. This creates an urgent need to remove the knowledge of problematic samples while preserving useful updates and source knowledge during online deployment, without access to historical raw inputs. To the best of our knowledge, existing machine unlearning methods do not address this problem. We first distinguish the two notions of untraining and unlearning, and address this need by proposing a novel untraining setting: Data-Free Untraining in TTA. For this setting, we propose a batch-level untraining method based on a parameter-efficient TTA structure that combines model-state storage, contrastive reconstruction, and selective untraining. We represent the unavailable requested data by reconstructed tokens and remove their knowledge via selective untraining while preserving the retained adaptation and source knowledge. We evaluate our method with differ- ent TTA methods on datasets commonly used in TTA. Our method outperforms all adapted MU baselines and maintains stable performance across successive untraining requests.