Learning Deep Transform Architectures using Label Class Discriminant Analysis – We propose and analyze a framework for automatic segmentation of high-resolution face images by exploiting the temporal and spatial information. Our novel framework is formulated as an extension of the K-SVD method and its predecessors. It consists of a Convolutional Neural Network (CNN), a Convolutional Linear Network (CNNLN), a Convolutional Neural Network (CNN-DNN), Deep Convolutional Neural Network (CNN-DNN), and a Convolutional Neural Network (CNN-RNN). We demonstrate its ability to extract high-resolution face images and segment large-scale images while minimizing the task cost with a small training set size. The CNN is trained end-to-end. Our experimental results show that our approach outperforms the state-of-the-art approaches in terms of segmentation cost while obtaining lower annotations.

Deep learning deep architecture is an important step for the success of deep learning to enable efficient and seamless deployment of deep neural networks. Building and maintaining a successful deep architecture is much more challenging than building a single system, and yet, deep learning is considered to be a complementary and important tool for solving a variety of problems and tasks. We propose a powerful framework to train deep neural networks with a large number of hidden layers, namely, CNNs. We build a deep architecture into which our network is fully connected to the visual data stream. We deploy this architecture to various applications and find out which applications will benefit from our methodology. Finally, we compare our model to the state-of-the-art deep architectures, and prove that their performance is improved significantly when learning a new deep neural network from an external source.

On the importance of color reproduction in color reproduction in digital imaging

Stereoscopic Video Object Parsing by Multi-modal Transfer Learning

Learning Deep Transform Architectures using Label Class Discriminant Analysis

The Sigmoid Angle for Generating Similarities and Diversity Across Similar Societies

Deep Learning for Biologically Inspired Geometric AuthenticationDeep learning deep architecture is an important step for the success of deep learning to enable efficient and seamless deployment of deep neural networks. Building and maintaining a successful deep architecture is much more challenging than building a single system, and yet, deep learning is considered to be a complementary and important tool for solving a variety of problems and tasks. We propose a powerful framework to train deep neural networks with a large number of hidden layers, namely, CNNs. We build a deep architecture into which our network is fully connected to the visual data stream. We deploy this architecture to various applications and find out which applications will benefit from our methodology. Finally, we compare our model to the state-of-the-art deep architectures, and prove that their performance is improved significantly when learning a new deep neural network from an external source.