Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Coupled Marginalized Auto-Encoders for Cross-Domain Multi-View Learning / 2125
Shuyang Wang, Zhengming Ding, Yun Fu

In cross-domain learning, there is a more challenging problem that the domain divergence involves more than one dominant factors, e.g., different view-points, various resolutions and changing illuminations. Fortunately, an intermediate domain could often be found to build a bridge across them to facilitate the learning problem. In this paper, we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem. Specifically, we design two marginalized denoising auto-encoders, one for the target and the other for source as well as the intermediate one. To better couple the two denoising auto-encoders learning, we incorporate a feature mapping, which tends to transfer knowledge between the intermediate domain and the target one. Furthermore, the maximum margin criterion, e.g., intra-class compactness and inter-class penalty, on the output layer is imposed to seek more discriminative features across different domains. Extensive experiments on two tasks have demonstrated the superiority of our method over the state-of-the-art methods.