Dictionary Learning, Super-Resolution and Texture Matching with Hashing Algorithm


Dictionary Learning, Super-Resolution and Texture Matching with Hashing Algorithm – With the proliferation of digital art, there have been numerous applications of unsupervised sparse learning to automatically estimate an object from a sparse representation using a deep convolutional network. We propose an unsupervised sparse estimation framework based on an iterative process of minimizing and discretizing the input data. Our algorithm achieves a fast reconstruction using linear convergence rates, a lower memory footprint, and higher accuracy than many state-of-the-art unsupervised sparse detection algorithms. We also show that the residuals of the object can be extracted by the encoder as a regularity function which is very useful for unsupervised learning. We then extend that sparse reconstruction procedure to an unsupervised setting where the reconstruction can take place offline. Further, we show that sparse reconstruction can lead to better performance in image classification, i.e. object detection and classification.

This paper presents a methodology for identifying user interests and preferences for user-generated content in Internet articles. We start by evaluating the impact of topics in user-generated articles in terms of articles’ relevance to users’ interests, and a quantitative study of this impact would be useful to facilitate user exploration of Internet articles.

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Dictionary Learning, Super-Resolution and Texture Matching with Hashing Algorithm

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  • Learning Deep Neural Networks for Multi-Person Action Hashing

    A Framework for Interpretable Machine Learning of Web Usage DataThis paper presents a methodology for identifying user interests and preferences for user-generated content in Internet articles. We start by evaluating the impact of topics in user-generated articles in terms of articles’ relevance to users’ interests, and a quantitative study of this impact would be useful to facilitate user exploration of Internet articles.


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