论文标题
locogan-当地卷积的gan
LocoGAN -- Locally Convolutional GAN
论文作者
论文摘要
在论文中，我们构建了一个完全卷积的gan模型：locogan，潜在空间由可能不同分辨率的噪声样图像给出。学习是本地的，即我们处理的不是整个噪声样图像，而是固定尺寸的子图像。结果，Locogan可以产生任意维度的图像，例如LSUN卧室数据集。我们方法的另一个优点来自我们使用位置通道的事实，即允许产生完全周期性的（例如圆柱形全景图像）或几乎是周期性的，无限长的“图像”（例如壁纸）。
In the paper we construct a fully convolutional GAN model: LocoGAN, which latent space is given by noise-like images of possibly different resolutions. The learning is local, i.e. we process not the whole noise-like image, but the sub-images of a fixed size. As a consequence LocoGAN can produce images of arbitrary dimensions e.g. LSUN bedroom data set. Another advantage of our approach comes from the fact that we use the position channels, which allows the generation of fully periodic (e.g. cylindrical panoramic images) or almost periodic ,,infinitely long" images (e.g. wall-papers).
