2D Convolutional neural network (CNN) has arguably become the de facto standard for computer vision tasks. Recent findings, however, suggest that CNN may not be the best option for 1D pattern recognition, especially for datasets with over 1 M training samples, e.g., existing CNN-based methods for 1D signals are highly reliant on human pre-processing. Common practices include utilizing discrete Fourier transform (DFT) to reconstruct 1D signal into 2D array. To add to extant knowledge, in this paper, a novel 1D data processing algorithm is proposed for 1D big data analysis through learning a deep deconvolutional-convolutional network. Rather than resorting to human-based techniques, we employed deconvolution layers to convert 1 D signals into 2D data. On top of the deconvolution model, the data was identified by a 2D CNN. Compared with the existing 1D signal processing algorithms, DCNet boasts the advantages of less human-made inference and higher generalization performance. Our experimental results from a varying number of training patterns (50 K to 11 M) from classification and regression demonstrate the desirability of our new approach.
翻译:2D 进化神经网络(CNN)可以说已成为计算机视觉任务的实际标准。但最近的调查结果表明,CNN也许不是1D模式识别的最佳选择,特别是1M以上培训样本的数据集,例如,现有CNN的1D信号使用的方法高度依赖人类预处理。常见做法包括利用离散Fourier变换(DFT)将1D信号重建为2D阵列。为了增加现有知识,本文件提议了1D新版本的1D数据处理算法,用于通过学习深层进化-进化网络进行1D大数据分析。我们不是采用人基技术,而是利用变化层将1D信号转换为2D数据。在变化模型之外,数据是由2DCNN所识别的。与现有的1D信号处理算法相比,DCNet能够证明人造推论较少和一般化性化表现的优点。我们从分类和回归中得出的不同培训模式(50K-11M)的实验结果显示了我们新方法的可取性。