Video Compression with Low Rank Tensor: A Survey


Video Compression with Low Rank Tensor: A Survey – 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.

While traditional CRT processors are designed to work with a single linear model, hybrid CRT processors provide a fully integrated model that can be generalized in any way. To overcome the problem of model selection, we suggest using a hybrid CRT model for the tasks of model selection and training. As input to the hybrid CRT model is the number of attributes, we propose a discriminative CRT model that can identify the most discriminative attributes for a CRT model, which can be used for selection. We demonstrate that the proposed CRT model can generalize well to different domains and models.

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Video Compression with Low Rank Tensor: A Survey

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  • Learning from the Hindsight Plan: On Learning from Exact Time-series Data

    Learning with a Hybrid CRT ProcessorWhile traditional CRT processors are designed to work with a single linear model, hybrid CRT processors provide a fully integrated model that can be generalized in any way. To overcome the problem of model selection, we suggest using a hybrid CRT model for the tasks of model selection and training. As input to the hybrid CRT model is the number of attributes, we propose a discriminative CRT model that can identify the most discriminative attributes for a CRT model, which can be used for selection. We demonstrate that the proposed CRT model can generalize well to different domains and models.


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