To obtain invariant representations, domain-adversarial training has been widely employed, and it is also frequently used for a variety of domain generalization (DG) tasks. For supervised learning tasks such as classification, emerging techniques that converge to smooth optima have demonstrated increased generalization. In this study, we examine how smoothness-enhancing formulations affect DG, which aims to combine task loss with adversarial terms. We consider that stabilizing the adversarial training leads to superior performance on the target domain when converging to a smooth minima with respect to task loss. Contrary to task loss, our analysis demonstrates that smooth minima convergence with respect to adversarial loss results in suboptimal generalization on the target domain. The investigation led us to develop the adaptive sharpness-aware minimization (ASAM) for adversarial DG method, which significantly improves the performance of current domain-adversarial methods for classification tasks. The proposed strategy performs similarly to the traditional state-of-the-art options in the most recent benchmark, Digits-DG, PACS, Office-Home, and DomainNet.

With the increasing volume of healthcare data, automated International Classification of Diseases (ICD) has become increasingly relevant and is frequently regarded as a medical multi-label prediction problem. Current methods struggle to accurately classify medical diagnosis texts that represent deep and sparse categories. Unlike these works that model the label with code hierarchy or description for label prediction, we argue that the label generation with structural information can provide more comprehensive knowledge based on the observation that label synonyms and parent-child relationships in vary from their context in clinical contexts. In this study, we introduce \tool, a heterogeneous graph model with improved attention for automated ICD coding. Notably, our approach represents the model to consider this task as a labelled graph generation problem. Our enhanced attention mechanism boosts the model's capacity to learn from multi-relational heterogeneous graph representations. Additionally, we propose a discriminator for labelled graphs (LG) that computes the reward for each ICD code in the labelled graph generator. Our experimental findings demonstrate that our proposed model significantly outperforms all existing strong baseline methods and attains the best performance on three benchmark datasets.