论文标题
改进的训练单位图剂的技术
Improved Techniques for Training Single-Image GANs
论文作者
论文摘要
最近，人们对从单个图像学习生成模型的潜力引起了人们的兴趣，而不是从大型数据集中学习。此任务具有实际意义，因为这意味着生成模型可以用于收集大数据集不可行的域中。但是，训练能够仅从单个样本中生成逼真图像的模型是一个困难的问题。在这项工作中，我们进行了许多实验，以了解训练这些方法的挑战，并提出了一些最佳实践，我们发现我们可以比以前在该领域的工作产生改进的结果。一个关键部分是，与以前的单图生成方法不同，我们同时以一个顺序的多阶段方式训练多个阶段，从而使我们能够以更少的增加图像分辨率学习模型。与最近的最新基线状态相比，我们的模型的训练速度更快六倍，参数较少，并且可以更好地捕获图像的全局结构。
Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is of practical significance, as it means that generative models can be used in domains where collecting a large dataset is not feasible. However, training a model capable of generating realistic images from only a single sample is a difficult problem. In this work, we conduct a number of experiments to understand the challenges of training these methods and propose some best practices that we found allowed us to generate improved results over previous work in this space. One key piece is that unlike prior single image generation methods, we concurrently train several stages in a sequential multi-stage manner, allowing us to learn models with fewer stages of increasing image resolution. Compared to a recent state of the art baseline, our model is up to six times faster to train, has fewer parameters, and can better capture the global structure of images.
