DO-ALL Framework Uses Dataset Distillation to Stabilize Continual Test-Time Adaptation

DO-ALL distills source training data into compact synthetic anchors before deployment, then uses them to regularize continual test-time adaptation without retaining the original dataset. The plug-and-play design targets privacy-constrained deployments where source data can't be stored, addressing compounding error and catastrophic forgetting in long-term distribution shift.