Dictionary Learning for Scalable Image Classification


Dictionary Learning for Scalable Image Classification – As an image classification problem, the aim of a supervised neural network is to produce a large-dimensional feature vector representing the content of the input image. This is an important requirement for many state-of-the-art image classification systems, e.g., CNN, convolutional neural network, or 3D-CNN (3D-CNN). In this paper, we propose a novel class of CNNs with an energy function, which can learn feature representations for high-dimensional vectors using a novel type of stochastic gradient descent. With our objective function, the data matrix is chosen from a set of sparsely sampled samples and the data set is used as the intermediate representation of the feature vectors. The training set is efficiently used to learn the feature vector over high-dimensional vectors. The proposed method achieves state of the art performance on classification accuracy on datasets with over 40 million images. In addition, our method produces an unsupervised learning system which is very efficient and can learn a lot of feature vectors.

This paper presents a new framework for a unified framework for data analysis and representation of images with different types of nonlinear nonmonotonic transformations, including color, shape, and scale, using a novel and principled approach that combines nonlinear transformations with transformation-based learning techniques. The framework provides a powerful tool to improve the accuracy of a regression equation by leveraging local transformations, and has applications in image classification, texture synthesis, and object detection via a large-scale, nonlinear transformation graph.

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Dictionary Learning for Scalable Image Classification

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  • A Generative Adversarial Network for Sparse Convolutional Neural Networks

    A Unified Collaborative Strategy for Data Analysis and Feature ExtractionThis paper presents a new framework for a unified framework for data analysis and representation of images with different types of nonlinear nonmonotonic transformations, including color, shape, and scale, using a novel and principled approach that combines nonlinear transformations with transformation-based learning techniques. The framework provides a powerful tool to improve the accuracy of a regression equation by leveraging local transformations, and has applications in image classification, texture synthesis, and object detection via a large-scale, nonlinear transformation graph.


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