Before revision:  In this paper, we propose a novel network architecture to tackle these limitations by estimating mutual information in high- and low-level representationsregardless of the domain that is considered as a subject in this paper. Specifically, our proposed method extracts subject-invariant and class-relevant features, thereby enhancing generalizability in overall classification.
After revision:  In this paper, we propose a novel network architecture to tackle these limitations by estimating mutual information in high- and subject-invariant and class-relevant features, thereby enhancing generalizability in overall classification.