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Image compression with a hierarchical neural network

机译:使用分层神经网络进行图像压缩

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摘要

A neural network data compression method is presented. This network accepts a large amount of image or text data, compresses it for storage or transmission, and subsequently restores it when desired. A new training method, referred to as the Nested Training Algorithm (NTA), that reduces the training time considerably is presented. Analytical results are provided for the specification of the optimal learning rates and the size of the training data for a given image of specified dimensions. Performance of the network has been evaluated using both synthetic and real-world data. It is shown that the developed architecture and training algorithm provide high compression ratio and low distortion while maintaining the ability to generalize, and is very robust as well
机译:提出了一种神经网络数据压缩方法。该网络接受大量图像或文本数据,对其进行压缩以进行存储或传输,然后在需要时将其还原。提出了一种新的训练方法,称为嵌套训练算法(NTA),该方法大大减少了训练时间。提供了用于指定最佳尺寸的给定图像的最佳学习率和训练数据大小的分析结果。网络的性能已使用综合和真实数据进行了评估。结果表明,所开发的体系结构和训练算法在保持泛化能力的同时,提供了高压缩比和低失真,并且非常健壮。

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