SVDD: Single-view Video Dense Deformation Variation Based on Histogram and Line Filtering


SVDD: Single-view Video Dense Deformation Variation Based on Histogram and Line Filtering – Learning effective feature representations is one of the primary challenges in this field of learning visual feature representations for medical domains. In this paper, we propose a new deep learning approach for image classification in the context of feature learning. Our deep learning based approach works on the CNN network to classify images based on the features extracted from the images and then use these features for classification. To train CNNs, we use a fully convolutional-coherent architecture. We use the ConvNet architecture to perform the classification in three different settings: for the first setting we use a single ConvNet or a new convolutional-coherent architecture. In order to increase classification accuracy, we use three different kinds of convolutional-coherent architecture, the Fully Convolutional, Normalized and Normalized and propose a semi-supervised approach for classifying images using the CNNs. Experimental evaluation on four ImageNet benchmark datasets shows that our approach has superior performance compared to traditional method for classification accuracy and classification speed.

Existing training metrics used for continuous time series analysis are not very robust. We show that even though the metric uses Gaussian processes, this metric is not quite appropriate for continuous time series analysis, so it is necessary to learn it to be robust. We propose a new framework that applies the metric for continuous time series analysis using three different representations. Each representation is inspired by a latent Dirichlet process of a data graph. The representation, which is shown to be robust (as opposed to regularized), is then learned by minimizing the penalized mean squared error (MSE), in order to reduce the training error. It is theoretically justified to employ this framework for continuous time series analysis, but not for continuous time series. The proposed framework for continuous time series analysis is described in the supplementary article. The framework is designed to be lightweight and flexible, and will be useful to some new applications, such as prediction in a social network based data analysis.

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SVDD: Single-view Video Dense Deformation Variation Based on Histogram and Line Filtering

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    Faster Rates for the Regularized Loss Modulation on Continuous DataExisting training metrics used for continuous time series analysis are not very robust. We show that even though the metric uses Gaussian processes, this metric is not quite appropriate for continuous time series analysis, so it is necessary to learn it to be robust. We propose a new framework that applies the metric for continuous time series analysis using three different representations. Each representation is inspired by a latent Dirichlet process of a data graph. The representation, which is shown to be robust (as opposed to regularized), is then learned by minimizing the penalized mean squared error (MSE), in order to reduce the training error. It is theoretically justified to employ this framework for continuous time series analysis, but not for continuous time series. The proposed framework for continuous time series analysis is described in the supplementary article. The framework is designed to be lightweight and flexible, and will be useful to some new applications, such as prediction in a social network based data analysis.


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