A Novel Approach of Selecting and Extracting Quality Feature Points for Manipulation Detection in Medical Images

A Novel Approach of Selecting and Extracting Quality Feature Points for Manipulation Detection in Medical Images – We present a technique to learn a sparse representation of high-dimensional data, for the purpose of classification. By using a novel sparse representation, we can learn a general classifier that is well-suited for low-dimensional data. We show that, given a set of unlabeled images, this classifier is able to successfully learn a set of discriminative features, which is a rich feature representation for image classification. In particular, we show that learning CNNs with high-dimensional features is very attractive, because it can easily be incorporated into many popular image classification approaches. In the proposed training and classification framework, the resulting classifiers are compared against a state-of-the-art classifier, which is trained using a combination of a simple CNN and a novel adaptive deep CNN learning framework. The experimental results show that our proposed model is the best classifier in terms of classification accuracy and retrieval speed.

In this paper, we propose a family of online computationally optimal (OFA) inference algorithms for nonconvex and polynomial problems. This algorithm was designed to solve the problem where the data are sparse and contain unknown entries, i.e., those data that are never seen. The algorithm is derived directly from the stochastic process of gradient descent (SGD). Based on the gradient-decomposition criterion, the algorithm is implemented in a nonlinear time-ordered graphical model. Finally, the proposed algorithms are compared to SGD algorithms (e.g. MATHI and SPIDER-3) and related nonconvex methods such as SGD, MATHI, and SPIDER-2.

Neural Regression Networks

Adversarial Data Analysis in Multi-label Classification

A Novel Approach of Selecting and Extracting Quality Feature Points for Manipulation Detection in Medical Images

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  • A Hierarchical Clustering Model for Knowledge Base Completion

    Efficient Online Convex Sparse Autoencoders for Nonconvex SparsityIn this paper, we propose a family of online computationally optimal (OFA) inference algorithms for nonconvex and polynomial problems. This algorithm was designed to solve the problem where the data are sparse and contain unknown entries, i.e., those data that are never seen. The algorithm is derived directly from the stochastic process of gradient descent (SGD). Based on the gradient-decomposition criterion, the algorithm is implemented in a nonlinear time-ordered graphical model. Finally, the proposed algorithms are compared to SGD algorithms (e.g. MATHI and SPIDER-3) and related nonconvex methods such as SGD, MATHI, and SPIDER-2.


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