Deep Learning for Realtime Road Scattering by Generating Semantic Shapes on a Massive Texture Network


Deep Learning for Realtime Road Scattering by Generating Semantic Shapes on a Massive Texture Network – We propose a deep learning-based approach for extracting high-quality texture images of a scene from a large texture dataset. Our approach is trained on a texture dataset, and further trained on the deep network on a smaller dataset. For training, we train deep network to extract rich texture features and then use an algorithm based on the discriminative loss to classify the texture features. We show that our approach can significantly reduce the number of iterations required for training, and outperforms previous methods in image classification.

This paper investigates the possibility of improving the learning of recurrent neural networks by using convolutional neural networks to improve the learning of the visual sequence. Recent results on object classification have shown that object recognizers have considerable ability to discover the object category. However, these recognition models suffer from a poor representation and they do not support the learning task. In this paper, we propose to embed the object categories into a convolutional neural network for training recurrent neural network models. In particular, we embed a discriminator based approach into the convolutional neural network to encode the contextual labels in the network. Our method provides a large set of discriminators that can be learned to model object category. Experimental results on the ImageNet dataset show that the method works better than other baselines in terms of accuracy and learning rate.

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Deep Learning for Realtime Road Scattering by Generating Semantic Shapes on a Massive Texture Network

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  • A Bayesian Model of Cognitive Radio Communication Based on the SVM

    Video Compression with Low Rank Tensor: A SurveyThis paper investigates the possibility of improving the learning of recurrent neural networks by using convolutional neural networks to improve the learning of the visual sequence. Recent results on object classification have shown that object recognizers have considerable ability to discover the object category. However, these recognition models suffer from a poor representation and they do not support the learning task. In this paper, we propose to embed the object categories into a convolutional neural network for training recurrent neural network models. In particular, we embed a discriminator based approach into the convolutional neural network to encode the contextual labels in the network. Our method provides a large set of discriminators that can be learned to model object category. Experimental results on the ImageNet dataset show that the method works better than other baselines in terms of accuracy and learning rate.


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