Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks

Understand

Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change.

• The advancement of this area is challenged by: 1) characterizing data distribution drift and its impacts on models, 2) expressiveness in tracking the model dynamics, and 3) theoretical guarantee on the performance.
• To address them, we propose a Temporal Domain Generalization with Drift-Aware Dynamic Neural Network (DRAIN) framework.
• Specifically, we formulate the problem into a Bayesian framework that jointly models the relation between data and model dynamics.