Depression disorders are a major growing concern worldwide, especially given the unmet need for widely deployable depression screening for use in real-world environments. Speech-based depression screening technologies have shown promising results, but primarily in systems that are trained using laboratory-based recorded speech. They do not generalize well on data from more naturalistic settings. This paper addresses the generalizability issue by proposing multiple adaptation strategies that update pre-trained models based on a dilated convolutional neural network (CNN) framework, which improve depression detection performance in both clean and naturalistic environments. Experimental results on two depression corpora show that feature representations in CNN layers need to be adapted to accommodate environmental changes, and that increases in data quantity and quality are helpful for pre-training models for adaptation. The cross-corpus adapted systems produce relative improvements of 29.4% and 17.2% in unweighted average recall over non-adapted systems for both clean and naturalistic corpora, respectively.