Sparse and Accurate Image Classification by Exploiting the Optimal Entropy


Sparse and Accurate Image Classification by Exploiting the Optimal Entropy – In this manuscript we propose a novel approach to image-based semantic prediction which uses a new dataset with large-scale datasets with the ability to learn semantic information as inputs. We first learn the semantic information via a deep recurrent neural network, and we update this network using a learning-theory framework. We then apply our deep recurrent neural network to the semantic prediction task. We show that the learned semantic information and the learned visual features are complementary for a large variety of tasks with different semantic information. This suggests a significant improvement in semantic classification and semantic prediction over previous state-of-the-art visual recognition methods. Our neural network provides a simple approach to semantic prediction.

Machine learning has become as powerful as computer vision in many aspects. We present an algorithm for unsupervised classification of images. Our method is based on kernel-based algorithms, and in particular is based on using the kernel of an unsupervised deep neural network (DNN). After training the DNN, the model learns to label images with its corresponding kernel, and subsequently to update the model for each image. This update is performed in a supervised manner. The method is evaluated on two datasets (1,000 images and 1,000 unlabeled images), and results show that the update process is beneficial for unsupervised classification.

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Sparse and Accurate Image Classification by Exploiting the Optimal Entropy

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    Convolutional Kernel MachinesMachine learning has become as powerful as computer vision in many aspects. We present an algorithm for unsupervised classification of images. Our method is based on kernel-based algorithms, and in particular is based on using the kernel of an unsupervised deep neural network (DNN). After training the DNN, the model learns to label images with its corresponding kernel, and subsequently to update the model for each image. This update is performed in a supervised manner. The method is evaluated on two datasets (1,000 images and 1,000 unlabeled images), and results show that the update process is beneficial for unsupervised classification.


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