Jin Yang

Machine learning and deep learning models trained on a source domain often suffer from performance degradation when deployed to new target domains due to domain shifts arising from differences in data distributions, acquisition conditions, or temporal variations. Domain adaptation addresses this issue by transferring knowledge from labeled source data to unlabeled target data. However, acquiring labels for target-domain samples is often costly or impractical in real-world applications. To improve label efficiency, active domain adaptation (ADA) and active continual learning (ACL) integrate active learning strategies into domain adaptation and continual learning frameworks. ADA selectively queries informative target samples to enhance adaptation performance, while ACL extends this paradigm to sequential settings, enabling models to adapt to evolving data streams while mitigating catastrophic forgetting. This survey provides a systematic review of ADA and ACL, focusing on their advances and applications. We further examine extensions of ADA such as source-free ADA, integration with semi-supervised learning, and advanced techniques for handling challenging adaptation scenarios. In addition, we summarize applications across computer vision, medical imaging, robotics, natural language processing, scientific and engineering tasks. Finally, we discuss open challenges and future directions, including robust adaptation under complex distribution shifts and reliable semisupervised adaptation.

Machine learning and deep learning models trained on a source domain often suffer from performance degradation when deployed to new target domains due to domain shifts arising from differences in data distributions, acquisition conditions, or temporal variations. Domain adaptation addresses this issue by transferring knowledge from labeled source data to unlabeled target data. However, acquiring labels for target-domain samples is often costly or impractical in real-world applications. To improve label efficiency, active domain adaptation (ADA) and active continual learning (ACL) integrate active learning strategies into domain adaptation and continual learning frameworks. ADA selectively queries informative target samples to enhance adaptation performance, while ACL extends this paradigm to sequential settings, enabling models to adapt to evolving data streams while mitigating catastrophic forgetting. This survey provides a systematic review of ADA and ACL, focusing on their advances and applications. We further examine extensions of ADA such as source-free ADA, integration with semi-supervised learning, and advanced techniques for handling challenging adaptation scenarios. In addition, we summarize applications across computer vision, medical imaging, robotics, natural language processing, scientific and engineering tasks. Finally, we discuss open challenges and future directions, including robust adaptation under complex distribution shifts and reliable semi-supervised adaptation.

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions.