3,491 research outputs found

    Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning

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    The capability of the traditional semi-supervised learning (SSL) methods is far from real-world application since they do not consider (1) class imbalance and (2) class distribution mismatch between labeled and unlabeled data. This paper addresses such a relatively under-explored problem, imbalanced semi-supervised learning, where heavily biased pseudo-labels can harm the model performance. Interestingly, we find that the semantic pseudo-labels from a similarity-based classifier in feature space and the traditional pseudo-labels from the linear classifier show the complementary property. To this end, we propose a general pseudo-labeling framework to address the bias motivated by this observation. The key idea is to class-adaptively blend the semantic pseudo-label to the linear one, depending on the current pseudo-label distribution. Thereby, the increased semantic pseudo-label component suppresses the false positives in the majority classes and vice versa. We term the novel pseudo-labeling framework for imbalanced SSL as Distribution-Aware Semantics-Oriented (DASO) Pseudo-label. Extensive evaluation on CIFAR10/100-LT and STL10-LT shows that DASO consistently outperforms both recently proposed re-balancing methods for label and pseudo-label. Moreover, we demonstrate that typical SSL algorithms can effectively benefit from unlabeled data with DASO, especially when (1) class imbalance and (2) class distribution mismatch exist and even on recent real-world Semi-Aves benchmark.Comment: "Code: https://github.com/ytaek-oh/daso

    Bucillamine prevents cisplatin-induced ototoxicity through induction of glutathione and antioxidant genes.

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    Bucillamine is used for the treatment of rheumatoid arthritis. This study investigated the protective effects of bucillamine against cisplatin-induced damage in auditory cells, the organ of Corti from postnatal rats (P2) and adult Balb/C mice. Cisplatin increases the catalytic activity of caspase-3 and caspase-8 proteases and the production of free radicals, which were significantly suppressed by pretreatment with bucillamine. Bucillamine induces the intranuclear translocation of Nrf2 and thereby increases the expression of γ-glutamylcysteine synthetase (γ-GCS) and glutathione synthetase (GSS), which further induces intracellular antioxidant glutathione (GSH), heme oxygenase 1 (HO-1) and superoxide dismutase 2 (SOD2). However, knockdown studies of HO-1 and SOD2 suggest that the protective effect of bucillamine against cisplatin is independent of the enzymatic activity of HO-1 and SOD. Furthermore, pretreatment with bucillamine protects sensory hair cells on organ of Corti explants from cisplatin-induced cytotoxicity concomitantly with inhibition of caspase-3 activation. The auditory-brainstem-evoked response of cisplatin-injected mice shows marked increases in hearing threshold shifts, which was markedly suppressed by pretreatment with bucillamine in vivo. Taken together, bucillamine protects sensory hair cells from cisplatin through a scavenging effect on itself, as well as the induction of intracellular GSH

    Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach

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    Constructing an organized dataset comprised of a large number of images and several captions for each image is a laborious task, which requires vast human effort. On the other hand, collecting a large number of images and sentences separately may be immensely easier. In this paper, we develop a novel data-efficient semi-supervised framework for training an image captioning model. We leverage massive unpaired image and caption data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples via Generative Adversarial Networks to learn the joint distribution of image and caption. To evaluate, we construct scarcely-paired COCO dataset, a modified version of MS COCO caption dataset. The empirical results show the effectiveness of our method compared to several strong baselines, especially when the amount of the paired samples are scarce.Comment: EMNLP 2019. Project page : https://sites.google.com/view/emnlp19scarcecaptio

    Preliminary evidence for the psychophysiological effects of technologic feature in e-commerce

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    As information and communication technologies are advanced, consumers are now able to enhance their e-commerce experiences regardless the channel, and it leads fashion retailers to develop better innovative experiential strategy to secure sustainable competency. The purpose of this study is to focus on apparel website to investigate the effect of branded contents on consumer\u27s pleasure and arousal that in turn may influence consumer\u27s response behaviors. This study employed S-O-R paradigm which explains that consumers\u27 inner organisms change according to the exposed external stimulation, and the changes antedate behavioral responses. Pleasure and arousal were measured with BioPAC MP150, which indicates the changes of electromyogram (EMG: pleasure), galvanic skin reflex (GSR: arousal), and heart rate (HR: pleasure) follow by the self-reported survey about behavioral responses. This study found that the effect for e-commerce\u27s branded content video on consumer\u27s response is indirect, and change of arousal is an indicator of hedonic shopping behavior
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