Domain adaptation (DA) and domain generalization (DG) have emerged as a solution to the domain shift problem where the distribution of the source and target data is different. The task of DG is more challenging than DA as the target data is totally unseen during the training phase in DG scenarios. The current state-of-the-art employs adversarial techniques, however, these are rarely considered for the DG problem. Furthermore, these approaches do not consider correlation alignment which has been proven highly beneficial for minimizing domain discrepancy. In this paper, we propose a correlation-aware adversarial DA and DG framework where the features of the source and target data are minimized using correlation alignment along with adversarial learning.Incorporating the correlation alignment module along with adversarial learning helps to achieve a more domain agnostic model due to the improved ability to reduce domain discrepancy with unlabeled target data more effectively. Experiments on benchmark datasets serve as evidence that our proposed method yields improved state-of-the-art performance.

Domain adaption (DA) and domain generalization (DG) are two closely related methods which are both concerned with the task of assigning labels to an unlabeled data set. The only dissimilarity between these approaches is that DA can access the target data during the training phase, while the target data is totally unseen during the training phase in DG. The task of DG is challenging as we have no earlier knowledge of the target samples. If DA methods are applied directly to DG by a simple exclusion of the target data from training, poor performance will result for a given task. In this paper, we tackle the domain generalization challenge in two ways. In our first approach, we propose a novel deep domain generalization architecture utilizing synthetic data generated by a Generative Adversarial Network (GAN). The discrepancy between the generated images and synthetic images is minimized using existing domain discrepancy metrics such as maximum mean discrepancy or correlation alignment. In our second approach, we introduce a protocol for applying DA methods to a DG scenario by excluding the target data from the training phase, splitting the source data to training and validation parts, and treating the validation data as target data for DA. We conduct extensive experiments on four cross-domain benchmark datasets. Experimental results signify our proposed model outperforms the current state-of-the-art methods for DG.

Vietnamese populations in Vietnam and the United States have a high prevalence of smoking. The associations among behavioral risk factors, acculturation, and smoking among the Vietnamese population living in the United States are not well documented. The present study aimed to identify the factors associated with smoking behavior among Vietnamese men living in Santa Clara County, California. A cross-sectional random-digit-dialed telephone survey was conducted. The sampling frame consisted of 27 Vietnamese surnames from the Santa Clara County telephone directory. A total of 660 adult respondents were interviewed to collect information on general health status, alcohol and tobacco use, HIV/AIDS, sexual behavior, injury control, hypertension, cholesterol screening, and acculturation. Of the 660 adults interviewed, 364 (55.2%) were male and 296 (44.8%) were female. Among males, 31.9% were current smokers, and among females, only one woman reported smoking. Univariate analyses revealed that having less than a college education, having poor English language skills, using Vietnamese at home and with friends, being less acculturated, not having a routine physical or blood cholesterol check, and being a binge drinker were significantly associated with an increased likelihood of smoking. Multivariate analysis revealed two independently associated factors: Respondents who were more acculturated were less likely to smoke (OR = 0.38, 95% CI = 0.18-0.83), and those not having cholesterol checked were more likely to smoke (OR = 2.48, 95% CI = 1.30-4.71). Acculturation level was inversely associated with smoking among Vietnamese adult men in Santa Clara County. Other health risk behaviors coexisted with smoking behavior and should be considered in prevention programs.

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