GeAE: GAE-Embedded Autoencoder Based Causal Representation for Robust Domain Adaptation
Kuang Zhou, Ming Zhu Jiang, Bogdan Gabryś · 2023
In this work, we study the unsupervised robust domain adaptation problem where only a single well labeled source domain data is available during the learning process. A new causal representation method based on a Graph autoen-coder embedded AutoEncoder, named GeAE, is introduced to learn invariant representations across domains for robust domain adaption. The proposed method can handle nonlinear causal relations included in the data by a causal structure learning process similar to a graph autoencoder. Moreover, the cross-entropy loss as well as the causal structure loss and the reconstruction loss are incorporated in the objective function designed in a united autoencoder to improve the quality of predictions using causal representations. Experimental results on one generated dataset and three real-world datasets demonstrate the effectiveness of GeAE in comparison with the state-of-the-art methods.