Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner. Here, we present a new architecture for image generators, where the color value at each pixel is computed independently given the value of a random latent vector and the coordinate of that pixel. No spatial convolutions or similar operations that propagate information across pixels are involved during the synthesis. We analyze the modeling capabilities of such generators when trained in an adversarial fashion, and observe the new generators to achieve similar generation quality to state-of-the-art convolutional generators. We also investigate several interesting properties unique to the new architecture.

翻译：现有图像生成网络严重依赖空间相变和可选的自省区块, 以便以粗略到纯度的方式逐步合成图像。 这里, 我们为图像生成者展示了一个新的结构, 每一个像素的颜色值都是独立计算, 取决于随机潜在矢量的值和像素的坐标。 合成过程中没有涉及空间相变或类似的在像素之间传播信息的操作。 我们分析这些生成者在接受对抗式培训时的模型能力, 并观察新的生成者, 以达到与最先进的像素生成者相似的生成质量。 我们还调查了新结构中独有的几个有趣的特性 。