论文标题
任意规模的图像合成
Arbitrary-Scale Image Synthesis
论文作者
论文摘要
位置编码已使最近的工作能够训练一个可以生成不同尺度图像的对抗网络。但是，这些方法要么仅限于一组离散量表，要么努力在未明确训练的尺度上保持良好的感知质量。我们提出了对生成器层转换不变的比例一致的位置编码的设计。这即使在训练期间看不见的尺度上，也可以产生任意尺度的图像。此外，我们将新颖的尺度增强量纳入我们的管道和部分生成训练中，以促进在任意尺度上综合图像的合成。最后，我们在各种常用数据集上显示了量表的连续体显示竞争结果。
Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator's layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis.
