Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In this paper we propose to revert to learning combinations of preset spectral filters by switching to CNNs with harmonic blocks. We rely on the use of the Discrete Cosine Transform (DCT) filters which have excellent energy compaction properties and are widely used for image compression. The proposed harmonic blocks rely on DCT-modeling and replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. We demonstrate how the harmonic networks can be efficiently compressed in a straightforward manner by truncating high-frequency information in harmonic blocks which is possible due to the redundancies in the spectral domain. We report extensive experimental validation demonstrating the benefits of the introduction of harmonic blocks into state-of-the-art CNN models in image classification, segmentation and edge detection applications.

中文翻译：

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基于离散余弦变换的谐波卷积网络

卷积神经网络 (CNN) 学习过滤器以捕获特征空间中的局部相关模式。在本文中，我们建议通过切换到具有谐波块的 CNN 来恢复学习预设光谱滤波器的组合。我们依赖于离散余弦变换 (DCT) 滤波器的使用，该滤波器具有出色的能量压缩特性并广泛用于图像压缩。提出的谐波块依赖于 DCT 建模并取代传统的卷积层，以产生新的或现有的 CNN 架构的部分或完全谐波版本。我们展示了如何通过截断谐波块中的高频信息以直接的方式有效地压缩谐波网络，这可能是由于谱域中的冗余造成的。