An automatic diagnostic network using skew-robust adversarial discriminative domain adaptation to evaluate the severity of depression

Abstract

Background and ObjectiveDeep learning provides an automatic and robust solution to depression severity evaluation. However, despite it is powerful, there is a trade-off between robust performance and the cost of manual annotation. MethodsMotivated by knowledge evolution and domain adaptation, we propose a deep evaluation network using skew-robust adversarial discriminative domain adaptation (SRADDA), which adaptively shifts its domain from a large-scale Twitter dataset to a small-scale depression interview dataset for evaluating the severity of depression. ResultsWithout top-down selection, SRADDA-based severity evaluation network achieves regression errors of 6.38 (Root Mean Square Error,RMSE) and 4.93 (Mean Absolute Error,MAE), which outperforms baselines provided by the Audio/Visual Emotion Challenge and Workshop(AVEC 2017). However, with top-down selection, the network achieves comparable results (RMSE = 5.13, MAE = 4.08). ConclusionsResults show that SRADDA not only represents features robustly, but also performs comparably to state-of-the-art results on small-scale dataset, DAIC-WOZ.