In the cross-domain image classification scenario, domain adaption aims to address the challenge of transferring the knowledge obtained from the source domain to the target domain that is regarded as similar but different from the source domain. To get more reliable domain invariant representations, recent methods start to consider class-level distribution alignment across the source and target domains by adaptively assigning pseudo target labels. However, these approaches are vulnerable to the error accumulation and hence unable to preserve cross-domain category consistency. Because the accuracy of pseudo labels cannot be guaranteed explicitly. In this paper, we propose Adversarial Domain Adaptation with Semantic Consistency (ADASC) model to align the discriminative features across domains progressively and effectively, via exploiting the class-level relations between domains. Specifically, to simultaneously alleviate the negative influence of the false pseudo-target labels and get the discriminative domain invariant features, we introduce an Adaptive Centroid Alignment (ACA) strategy and a Class Discriminative Constraint (CDC) step to complement each other iteratively and alternatively in an end-to-end framework. Extensive experiments are conducted on several unsupervised domain adaptation datasets, and the results show that ADASC outperforms the state-of-the-art methods.

Electricity demand forecasting is a very important problem for energy supply and environmental protection. It can be formalized as a hierarchical time series forecasting problem with the aggregation constraints according to the geographical hierarchy, since the sum of the prediction results of the disaggregated time series should be equal to the prediction results of the aggregated ones. However in most previous work, the aggregation consistency is ensured at the loss of forecast accuracy. In this paper, we propose a novel clustering-based hierarchical electricity time series forecasting approach. Instead of dealing with the geographical hierarchy directly, we explore electricity consumption patterns by clustering analysis and build a new consumption pattern based time series hierarchy. We then present a novel hierarchical forecasting method with consumption hierarchical aggregation constraints to improve the electricity demand predictions of the bottom level, followed by a ``bottom-up" method to obtain forecasts of the geographical higher levels. Especially, we observe that in our consumption pattern based hierarchy the reconciliation error of the bottom level time series is ``correlated" to its membership degree of the corresponding cluster (consumption pattern), and hence apply this correlations as the regularization term in our forecasting objective function. Extensive experiments on real-life datasets verify that our approach achieves the best prediction accuracy, compared with the state-of-the-art methods.