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Real-world face recognition using a single sample per person (SSPP) is a challenging task. The problem is exacerbated if the conditions under which the gallery image and the probe set are captured are completely different. To address these issues from the perspective of domain adaptation, we introduce an SSPP domain adaptation network (SSPP-DAN). In the proposed approach, domain adaptation, feature extraction, and classification are performed jointly using a deep architecture with domain-adversarial training. However, the SSPP characteristic of one training sample per class is insufficient to train the deep architecture. To overcome this shortage, we generate synthetic images with varying poses using a 3D face model. Experimental evaluations using a realistic SSPP dataset show that deep domain adaptation and image synthesis complement each other and dramatically improve accuracy. Experiments on a benchmark dataset using the proposed approach show state-of-the-art performance. All the dataset and the source code can be found in our online repository (https://github.com/csehong/SSPP-DAN).

Higher disease activity in SLE and initial severe neurologic deficits might be associated with the poor outcome of ATM. Corticosteroid slowly tapering-off therapy might be helpful in preventing the recurrence of ATM.

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Systemic lupus erythematosus (SLE) is a systemic, autoimmune disease that can involve virtually any organ of the body. Lupus nephritis (LN), the clinical manifestation of this disease in the kidney, is one of the most common and severe outcomes of SLE. Although a key pathological hallmark of LN is glomerular inflammation and damage, tubulointerstitial lesions have been recognized as an important component in the pathology of LN. Renal tubular epithelial cells are resident cells in the tubulointerstitium that have been shown to play crucial roles in various acute and chronic kidney diseases. In this context, recent progress has been made in examining the functional role of tubular epithelial cells in LN pathogenesis. This review summarizes recent advances in our understanding of renal tubular epithelial cells in LN, the potential role of tubular epithelial cells as biomarkers in the diagnosis, prognosis, and treatment of LN, and the future therapeutic potential of targeting the tubulointerstitium for the treatment of patients with LN.

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Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in real cases and possibly causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. To avoid accessing source data which may contain sensitive information, we introduce source data-free domain adaptation (SFDA). Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner. We observe that target samples with lower self-entropy measured by the pre-trained source model are more likely to be classified correctly. From this, we select the reliable samples with the self-entropy criterion and define these as class prototypes. We then assign pseudo labels for every target sample based on the similarity score with class prototypes. Further, to reduce the uncertainty from the pseudo labeling process, we propose set-to-set distance-based filtering which does not require any tunable hyperparameters. Finally, we train the target model with the filtered pseudo labels with regularization from the pre-trained source model. Surprisingly, without direct usage of labeled source samples, our SFDA outperforms conventional domain adaptation methods on benchmark datasets. Our code is publicly available at https://github.com/youngryan1993/SFDA-Domain-Adaptation-without-Source-Data.