With the blessing of drift-aware dynamic neural network, the new framework of time domain generalization far exceeds the domain generalization & adaptation method.

In the Domain Generalization (DG) task, when the distribution of the domain continuously changes with the environment, how to accurately capture the change and its impact on the model is a very important but also extremely challenging issue. To this end, Professor Zhao Liang's team from Emory University proposed a time domain generalization framework DRAIN based on Bayesian theory, which uses recursive networks to learn the drift of time dimension domain distribution, and combines dynamic neural networks and graph generation technology. Maximize the expression ability of the model and achieve model generalization and prediction in unknown fields in the future. This work has been selected into ICLR 2023 Oral (Top 5% among accepted papers).

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