While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namelyconsistency and complementarity network(CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet.

Since the financial crisis in late 2008-2009, several global regulatory authorities have mandated stress-testing exercises to evaluate the potential capital shortfalls & systemic impacts that large banks may face during adverse economic conditions. Thus, having the ability to analyze economic conditions & banking performance profiles together to determine relationships among their respective features may provide insights for stress testing tasks. In this paper, we propose an Integrated Multimodal Bank Stress Test Prediction (IMBSTP) model framework consisting of a two-stages; (1) economic conditions estimator to approximate joint representation among the exogenous factors using generative models, (2) bank capital & loss forecaster to project stress test measures based on dimensional & temporal features selected from the exogenous economic conditions & banking performance profiles using a dual-attention recurrent neural network. Extensive experimentation is performed on historical economic conditions & consolidated financial statements of U.S. bank holdings companies to show the effectiveness of our approach when compared to state-of-the-art baseline methods.