Fully-Fusion Image Restoration with Multi-Resolution Convolutional Sparse Coding


Fully-Fusion Image Restoration with Multi-Resolution Convolutional Sparse Coding – In this paper, we propose a simple and flexible framework of convolutional neural network (CNN) models that exploit local attention. The model constructs a representation from a set of local features in an iterative process, which are then utilized to reconstruct the target feature representation for the whole network. The main problem arising in CNNs is to estimate an attention vector for each object, while ignoring any attention between them. To overcome this problem we propose a neural network model based on a multi-scale attention mechanism. This model employs features from the local features to learn global attention, which maps each multi-scale attention vector to an attention matrix. The model can generate object representations for the target feature representation, which are used to enhance semantic representations generated by the system. We have conducted extensive experiments on an image-by-image retrieval task. The model demonstrates remarkable performance on the task of image retrieval, outperforming the previous state of the art on all the test datasets.

In this article, we review the performance of a new learning-based method for the classification of binary classification problems. Our method is based on learning Bayes’ generalized log-Linear regression (LLRL) to classify data with a linear class model. In particular, we use a variational inference procedure to derive a Bayes projection from the log-Linear regression. Our method is shown to be effective for classification problems when the linear class model for the data is a linear LER model. Experimental results validate our method for classification problems that do not contain a linear class, such as classification under the presence of a binary class. To the best of our knowledge, this study is the first to test our method using binary data.

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Fully-Fusion Image Restoration with Multi-Resolution Convolutional Sparse Coding

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  • A Minimax Stochastic Loss Benchmark

    A Simple Bounding Box for Kernelized Log-Linear Regression and its ImplicationsIn this article, we review the performance of a new learning-based method for the classification of binary classification problems. Our method is based on learning Bayes’ generalized log-Linear regression (LLRL) to classify data with a linear class model. In particular, we use a variational inference procedure to derive a Bayes projection from the log-Linear regression. Our method is shown to be effective for classification problems when the linear class model for the data is a linear LER model. Experimental results validate our method for classification problems that do not contain a linear class, such as classification under the presence of a binary class. To the best of our knowledge, this study is the first to test our method using binary data.


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