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.

As a powerful tool, deep learning can be used to discover the underlying structure of a computer’s input, and thus to model the dynamics of the input. In this work, we develop an iterative strategy for the deep learning to map input states into the input, as well as an iterative strategy for learning the output structure. To achieve this goal, in this work we construct an ensemble of deep network models, with weights on each model. Experimental results demonstrate that the weights have significantly different roles in the output structure and learned weights are more effective than other weights when applied to the same task.

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Dictionary Learning for Scalable Image Classification

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  • Efficient Video Super-resolution via Finite Element Removal

    Training of Convolutional Neural NetworksAs a powerful tool, deep learning can be used to discover the underlying structure of a computer’s input, and thus to model the dynamics of the input. In this work, we develop an iterative strategy for the deep learning to map input states into the input, as well as an iterative strategy for learning the output structure. To achieve this goal, in this work we construct an ensemble of deep network models, with weights on each model. Experimental results demonstrate that the weights have significantly different roles in the output structure and learned weights are more effective than other weights when applied to the same task.


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