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SYSTEM AND METHOD FOR IMPROVING CONVOLUTIONAL NEURAL NETWORK-BASED MACHINE LEARNING MODELS

机译:改进卷积神经网络的机器学习模型的系统和方法

摘要

Improved convolutional neural network-based machine learning models are disclosed herein. A convolutional neural network is configured to decompose feature maps generated based on a data item to be classified. The feature maps are decomposed into a first and second subsets. The first subset is representative of high frequency components of the data item, and the second subset is representative of low frequency components of the data item. The second subset is upsampled and is combined with the first subset. The combined feature maps are convolved with a filter to extract a set of features associated with the data item. The first subset is also downsampled and combined with the second subset. The combined feature maps are convolved with a filter to extract another set of features. The data item is classified based on the sets of features extracted based on the convolution operations.
机译:本文公开了一种改进的基于卷积神经网络的机器学习模型。 卷积神经网络被配置为分解基于要分类的数据项生成的特征映射。 特征贴图被分解为第一和第二子集。 第一子集代表数据项的高频分量,第二子集代表数据项的低频分量。 第二子集被追随并与第一子集合结合。 组合的特征贴图是用过滤器卷积的,以提取与数据项相关联的一组特征。 第一子集还逐渐采样并与第二子集组合。 组合的特征映射与过滤器卷积以提取另一组功能。 基于基于基于卷积操作提取的功能集的数据项进行分类。

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