Before revision:  Specifically, our proposed method extracts subject-invariant and class-relevant features, thereby enhancing generalizability in overall classification.
After revision:  Specifically, our proposed method extracts subject-invariant feature representations in an information-theoretic manner, without using adversarial learning. To be specific, we devise two operational components in a deep network that explicitly estimate mutual information between feature representations; (1) to decompose features in an intermediate layer into class-relevant features, thereby enhancing generalizability in overall classification.