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.

We explore the problems of learning non-linear sublinear models (NNs) from unstructured inputs. While the quality of each node is often poor, its computational efficiency is significantly improved over the previous state of the art. We focus our analysis on two related problems, namely, finding an efficient and effective method for learning a non-linear model with partial observability. First, we propose a new sub-gradient method to deal with partial observability through a simple convex relaxation. Second, we propose an efficient and fast learning procedure for learning a non-linear model with partial observability. We show that the approximation to partial observability for this method is asymptotically guaranteed to converge to its optimal value. The resulting algorithm can be easily extended to consider the cases of a non-linear model with partially observability.

We present an algorithm for the task of learning sparse representations of data and their combinations with sparse constraints.

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

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  • On the Relation Between the Matrix Symmetry Transform and Image Restoration

    Proximal Methods for Learning Sparse Sublinear Models with Partial ObservabilityWe explore the problems of learning non-linear sublinear models (NNs) from unstructured inputs. While the quality of each node is often poor, its computational efficiency is significantly improved over the previous state of the art. We focus our analysis on two related problems, namely, finding an efficient and effective method for learning a non-linear model with partial observability. First, we propose a new sub-gradient method to deal with partial observability through a simple convex relaxation. Second, we propose an efficient and fast learning procedure for learning a non-linear model with partial observability. We show that the approximation to partial observability for this method is asymptotically guaranteed to converge to its optimal value. The resulting algorithm can be easily extended to consider the cases of a non-linear model with partially observability.

    We present an algorithm for the task of learning sparse representations of data and their combinations with sparse constraints.


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