Towards a real-time CNN end-to-end translation


Towards a real-time CNN end-to-end translation – Most of the previous works on the problem of inferring the meaning of phrases in English translations have only provided simple solutions when solving a particular translation problem, or when trying to translate a certain sentence in some languages. This paper proposes a new framework for translating phrases in English translations, namely, a graph-based translation problem. To do this, we design and optimize an interactive system in order to learn the structure of the graph from the translation process and how this structure is related to the sentence. To this end, a neural network architecture which can predict the meaning of phrases in a sentence is trained. The output of our system can be used in translation systems to learn the meaning of phrases in French language. The system has been validated as having good performance when compared to an existing translation system which has only learned the meaning of phrases from the translation process. The system has been tested on five different languages: English, German, French and Arabic. We have tested both the system and the system with different results, achieving good results, and outperforming state-of-the-art systems on English, on two different Arabic languages.

In this work, we demonstrate how to effectively extract high-quality videos from noisy images. We show our method learns a convolutional neural network, which is able to reconstruct the full frame videos in terms of spatio-temporal spatio-temporal features. In addition, we demonstrate how to reconstruct full frames, which effectively allows for the extraction of temporal features. The results are analyzed by a new deep learning platform which can learn discriminant functions from noisy videos. The results show that the proposed method is able to extract frames from videos containing a rich set of spatial features.

Deep Network Trained by Combined Deep Network Feature and Deep Neural Network

An Integrated Representational Model for Semantic Segmentation and Background Subtraction

Towards a real-time CNN end-to-end translation

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  • Optimal Convergence Rate for the GQ Lambek transform

    Sparse DCT for Video ClassificationIn this work, we demonstrate how to effectively extract high-quality videos from noisy images. We show our method learns a convolutional neural network, which is able to reconstruct the full frame videos in terms of spatio-temporal spatio-temporal features. In addition, we demonstrate how to reconstruct full frames, which effectively allows for the extraction of temporal features. The results are analyzed by a new deep learning platform which can learn discriminant functions from noisy videos. The results show that the proposed method is able to extract frames from videos containing a rich set of spatial features.


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