论文标题
DCT-CONV：具有离散余弦变换的卷积网络中的编码过滤器
DCT-Conv: Coding filters in convolutional networks with Discrete Cosine Transform
论文作者
论文摘要
卷积神经网络基于大量训练的重量。因此，它们通常是数据束缚，对过度训练敏感并缓慢学习。我们遵循研究线，在该研究的基础上，根据较少的训练参数确定卷积神经层的过滤器。在本文中，训练有素的参数定义了一个频谱，该频谱将转换为具有反离散余弦变换的卷积过滤器（IDCT，在JPEG中解压缩时也应用了相同的频率）。我们分析了如何关闭光谱的选定组件，从而减少了网络的训练重量的数量，从而影响其性能。我们的实验表明，用训练有素的DCT参数编码过滤器会导致对传统卷积的改善。同样，随着关闭这些参数的增加程度，网络的性能修改了这种方式的降低非常缓慢。在某些实验中，当关闭这些参数中的99.9％时，观察到良好的性能。
Convolutional neural networks are based on a huge number of trained weights. Consequently, they are often data-greedy, sensitive to overtraining, and learn slowly. We follow the line of research in which filters of convolutional neural layers are determined on the basis of a smaller number of trained parameters. In this paper, the trained parameters define a frequency spectrum which is transformed into convolutional filters with Inverse Discrete Cosine Transform (IDCT, the same is applied in decompression from JPEG). We analyze how switching off selected components of the spectra, thereby reducing the number of trained weights of the network, affects its performance. Our experiments show that coding the filters with trained DCT parameters leads to improvement over traditional convolution. Also, the performance of the networks modified this way decreases very slowly with the increasing extent of switching off these parameters. In some experiments, a good performance is observed when even 99.9% of these parameters are switched off.
