A Novel Approach of Clustering for Hybrid Deep Neural Network – In this paper, we propose a novel approach to inferring semantic and morphological information from visual sequences. Previous work only requires the prior knowledge of the visual sequence to be learned. We show how to perform this task using an active learning paradigm, which involves constructing a mapping from visual features to semantic concepts by a supervised learning algorithm. The goal of the approach is to identify the semantic regions within each pixel to be recognized by the visual feature mapping, then infer the visual features of these regions, for further inference of semantic concepts. The experimental results show that the proposed approach outperforms previous methods.

In this work, we present a general framework to model a deep neural network (DNN) using a mixture of two types of inputs, namely: a first-class convolutional network, where the weights of the learned neural networks are calculated by the combination of training and labeling data. In this way, we extend the existing DNNs, including those based on the traditional back-propagation method, to the convolutional and network-based settings. These DNNs can be trained on either a single- or multiple-frame training set, making it possible to learn both types of input simultaneously for each network. Since both models are trained on one network, we can learn the weights both for the network and for each batch of data. Experimental results have been made to show the usefulness of such a model on both image retrieval and classification tasks from large-scale image databases.

Density-based Shape Matching

The Power of Outlier Character Models

A Novel Approach of Clustering for Hybrid Deep Neural Network

A Survey on Multi-Agent Communication

Automatic Tuning of Deep Convolutional Neural Networks Using Group Variant Registration in Image SegmentationIn this work, we present a general framework to model a deep neural network (DNN) using a mixture of two types of inputs, namely: a first-class convolutional network, where the weights of the learned neural networks are calculated by the combination of training and labeling data. In this way, we extend the existing DNNs, including those based on the traditional back-propagation method, to the convolutional and network-based settings. These DNNs can be trained on either a single- or multiple-frame training set, making it possible to learn both types of input simultaneously for each network. Since both models are trained on one network, we can learn the weights both for the network and for each batch of data. Experimental results have been made to show the usefulness of such a model on both image retrieval and classification tasks from large-scale image databases.