论文标题
轻量级的长距离生成对抗网络
Lightweight Long-Range Generative Adversarial Networks
论文作者
论文摘要
在本文中，我们介绍了新颖的轻质生成对抗网络，这些网络可以有效地捕获图像生成过程中的长期依赖性，并以更简单的体系结构产生高质量的结果。为了实现这一目标，我们首先引入一个远程模块，从而使网络能够动态调整集中抽样像素的数量并增强采样位置。因此，它可以打破卷积算子的固定几何结构的限制，并在空间和通道方向上捕获远距离依赖性。同样，拟议的远程模块可以突出像素之间的负面关系，作为稳定训练的正规化。此外，我们提出了一种新一代策略，通过该策略将元数据引入图像生成过程中，以提供有关目标图像的基本信息，这些信息可以稳定并加快训练过程。我们的新型远程模块仅引入几个其他参数，并且很容易插入现有模型以捕获长期依赖性。广泛的实验证明了我们方法具有轻量级体系结构的竞争性能。
In this paper, we introduce novel lightweight generative adversarial networks, which can effectively capture long-range dependencies in the image generation process, and produce high-quality results with a much simpler architecture. To achieve this, we first introduce a long-range module, allowing the network to dynamically adjust the number of focused sampling pixels and to also augment sampling locations. Thus, it can break the limitation of the fixed geometric structure of the convolution operator, and capture long-range dependencies in both spatial and channel-wise directions. Also, the proposed long-range module can highlight negative relations between pixels, working as a regularization to stabilize training. Furthermore, we propose a new generation strategy through which we introduce metadata into the image generation process to provide basic information about target images, which can stabilize and speed up the training process. Our novel long-range module only introduces few additional parameters and is easily inserted into existing models to capture long-range dependencies. Extensive experiments demonstrate the competitive performance of our method with a lightweight architecture.
