Learning Inference-Time Drift Sensor-Actuator for Domain Generalization

In machine learning tasks, models trained in the source domain often suffer from performance degradation in the target domain due to domain drift or distribution shift. In this paper, we explore the concept of sensor-actuator design in adaptive control to address this domain drift problem and develop a new approach, called learning inference-time drift sensor-actuator (LIDSA) for domain generalization. The drift sensor network consists of a constraint network and a data converter. The constraint network is learned to extract a set of constraints in the source domain and sense the domain drift by detecting the deviation from these constraints, called constraint error, which is correlated with the classification error. The data converter network then maps this constraint error into an effective guidance signal, which can guide the actuator network to adjust the feature to achieve improved discrimination power and better generalization performance. Our extensive experimental results demonstrate that the proposed LIDSA approach improves the performance of domain generalization over the baseline method.