论文标题
通过多通道小波过滤噪声均质化，以使gan中的高保真样品产生
Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in GANs
论文作者
论文摘要
在典型的生成对抗网络（GAN）的发电机中，输入噪声以通过一系列卷积操作生成假样品。但是，当前的噪声产生模型仅取决于像素空间中的信息，这增加了接近目标分布的困难。幸运的是，长期已验证的小波转换能够分解图像中的多个光谱信息。在这项工作中，我们提出了一种新型的基于多通道小波的过滤方法，以应对此问题。当将小波反卷积层嵌入发电机中时，所得的GAN（称为Waveletgan）利用小波反卷积来学习具有多个通道的过滤，这可以通过平均操作有效地匀浆，以便生成较高的样品。我们通过开放的GAN基准工具对时尚摄影师，KMNIST和SVHN数据集进行了基准实验。结果表明，由于这些数据集中获得的最小的FID，Waveletgan在生成高保真样品方面具有出色的性能。
In the generator of typical Generative Adversarial Networks (GANs), a noise is inputted to generate fake samples via a series of convolutional operations. However, current noise generation models merely relies on the information from the pixel space, which increases the difficulty to approach the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. When embedding a wavelet deconvolution layer in the generator, the resultant GAN, called WaveletGAN, takes advantage of the wavelet deconvolution to learn a filtering with multiple channels, which can efficiently homogenize the generated noise via an averaging operation, so as to generate high-fidelity samples. We conducted benchmark experiments on the Fashion-MNIST, KMNIST and SVHN datasets through an open GAN benchmark tool. The results show that WaveletGAN has excellent performance in generating high-fidelity samples, thanks to the smallest FIDs obtained on these datasets.
