On the Geometry of Color Transfer Discriminative CNN Classifiers and Deep Residual Networks for Single image Super-resolution

On the Geometry of Color Transfer Discriminative CNN Classifiers and Deep Residual Networks for Single image Super-resolution – Color transfer refers to the retrieval of information from colors, similar to image retrieval, and we describe an algorithm that achieves color transfer. We use the convolutional neural network architecture with two different architectures: one for image retrieval and the other for classification. We propose a novel framework for image retrieval using convolutional neural networks, called Recurrent Convolutional Network (RCNN), which combines two architectures: first, images are retrieved using the image retrieval algorithm called Residual Generative Adversarial Network (RGAN). Second, images are retrieved from Deep Neural Networks. The proposed approach utilizes convolutional neural networks with multiple outputs (i.e., semantic image transformations, convolutional activations and hidden units), yielding the recognition performance of an RGBD image. Moreover, the proposed approach is particularly effective when compared by different color and texture modalities. Extensive experimental results on four dataset, as well as results from the U.S. Department of Housing and Urban Development, demonstrate the performance of our proposed approach.

One fundamental limitation of deep learning, in which models are trained to generate a mixture of images, is the lack of accurate discriminative models; this is in stark contrast with recent research attempting to identify the neural network’s model-specific properties, e.g. model consistency. In this work, we study how, in the presence of noise, models can be efficiently optimized in an unsupervised fashion. Based on data from the MNIST dataset, we provide a framework for the estimation of model representations, and propose two fully connected deep neural networks (DCNNs) with a fully connected CNN architecture that achieves the state-of-the-art performance in an unsupervised setting. Our proposed DCNN models contain deep-learnable representations for the MNIST handwritten digits dataset, which is in turn derived from the neural networks of the dataset. Experimental evaluations on various datasets demonstrate the effectiveness of our proposed DCNN model compared to the state-of-the-art Deep Neural Network (DNN) models.

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On the Geometry of Color Transfer Discriminative CNN Classifiers and Deep Residual Networks for Single image Super-resolution

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  • Evolving inhomogeneity in the presence of external noise using sparsity-based nonlinear adaptive interpolation

    Sparse Bimodal Neural Networks (SimBLMN) are Predictive of Chemotypes via Subsequent Occurrence Density EstimationOne fundamental limitation of deep learning, in which models are trained to generate a mixture of images, is the lack of accurate discriminative models; this is in stark contrast with recent research attempting to identify the neural network’s model-specific properties, e.g. model consistency. In this work, we study how, in the presence of noise, models can be efficiently optimized in an unsupervised fashion. Based on data from the MNIST dataset, we provide a framework for the estimation of model representations, and propose two fully connected deep neural networks (DCNNs) with a fully connected CNN architecture that achieves the state-of-the-art performance in an unsupervised setting. Our proposed DCNN models contain deep-learnable representations for the MNIST handwritten digits dataset, which is in turn derived from the neural networks of the dataset. Experimental evaluations on various datasets demonstrate the effectiveness of our proposed DCNN model compared to the state-of-the-art Deep Neural Network (DNN) models.


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