Robust Feature Selection with a Low Complexity Loss – In this paper, we develop a simple unsupervised framework for automatic classification based on the classification of high-dimensional features that is not constrained by the model parameters. Our method consists of a convolutional neural network and a recurrent encoder and decoder model. The recurrent encoder model is used for classification to maximize the sparse features and the dictionary decoder is learned to improve the sparse ones. The dictionary encoder model is used for classification by convolutional neural network (CNN) in order to estimate the sparse feature vector for each dimension of interest. A new CNN architecture is developed for the classification of high-dimensional features that is capable of learning the dictionary representations. Our method is tested with MNIST and CIFAR-10 datasets.
In this paper, we propose a deep attention-based semantic segmentation framework for face recognition. Specifically, we propose the framework, which is able to capture both semantic and geometric information of faces using an attention-based semantic segmentation model. Our framework is based on the recent contribution of Bhattacharyya and others to our work.
Fast k-means using Differentially Private Low-Rank Approximation for Multi-relational Data
Robust Feature Selection with a Low Complexity Loss
Borent Graph Structure Learning with Sparsity
Hierarchical face recognition using color and depth informationIn this paper, we propose a deep attention-based semantic segmentation framework for face recognition. Specifically, we propose the framework, which is able to capture both semantic and geometric information of faces using an attention-based semantic segmentation model. Our framework is based on the recent contribution of Bhattacharyya and others to our work.
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