论文标题
任何尺寸的gan：解决图像扫描问题的解决方案
Anysize GAN: A solution to the image-warping problem
论文作者
论文摘要
我们提出了一种新型的一般对抗网络（GAN），以解决深度学习的共同问题。我们开发了一种新颖的体系结构，可以应用于现有的潜在矢量基于GAN结构，该结构使他们可以生成任何大小的直通图像。现有的图像生成需要匹配尺寸的均匀图像。但是，公开可用的数据集（例如Imagenet）包含数千种不同尺寸。调整图像大小会导致变形并更改图像数据，而作为我们的网络不需要此预处理步骤。我们对标准数据加载技术进行了重大更改，以使任何尺寸图像都可以加载进行训练。我们还通过添加多个输入和一个新颖的动态调整层来通过两种方式修改网络。最后，我们对歧视者进行调整，以制定多种决议。这些更改可以允许在内存允许的情况下对多个分辨率数据集进行培训，而无需任何调整大小。我们在ISIC 2019皮肤病变数据集上验证结果。我们证明我们的方法可以成功地在不同尺寸的情况下成功生成逼真的图像，而无需发问题，保留和理解空间关系，同时保持特征关系。我们将在接受纸上发布源代码。
We propose a new type of General Adversarial Network (GAN) to resolve a common issue with Deep Learning. We develop a novel architecture that can be applied to existing latent vector based GAN structures that allows them to generate on-the-fly images of any size. Existing GAN for image generation requires uniform images of matching dimensions. However, publicly available datasets, such as ImageNet contain thousands of different sizes. Resizing image causes deformations and changing the image data, whereas as our network does not require this preprocessing step. We make significant changes to the standard data loading techniques to enable any size image to be loaded for training. We also modify the network in two ways, by adding multiple inputs and a novel dynamic resizing layer. Finally we make adjustments to the discriminator to work on multiple resolutions. These changes can allow multiple resolution datasets to be trained on without any resizing, if memory allows. We validate our results on the ISIC 2019 skin lesion dataset. We demonstrate our method can successfully generate realistic images at different sizes without issue, preserving and understanding spatial relationships, while maintaining feature relationships. We will release the source codes upon paper acceptance.
