Chih-Kuan Yeh

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The paradigm shift from shallow classifiers with hand-crafted features to endto-end trainable deep learning models has shown significant improvements on supervised learning tasks. Despite the promising power of deep neural networks (DNN), how to alleviate overfitting during training has been a research topic of interest. In this paper, we present a(More)
Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label(More)
In this paper, we propose a graph-based image retrieval algorithm via query and database specific feature fusion. While existing feature fusion approaches exist for image retrieval, they typically do not consider the image database of interest (i.e., to be retrieved) for observing the associated feature contributions. In the offline learning stage, our(More)
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