论文标题
DAM-GAN：基于假纹理检测的动态注意图的图像插图
DAM-GAN : Image Inpainting using Dynamic Attention Map based on Fake Texture Detection
论文作者
论文摘要
深度神经的进步最近为图像介绍的领域带来了显着的图像合成性能。尤其是生成对抗网络（GAN）的适应已加速了高质量图像重建的显着进展。但是，尽管已经提出了许多基于GAN的网络进行图像插入，但在生成过程中，综合图像中仍然存在像素伪影或颜色不一致的情况，通常称为假纹理。为了减少虚假纹理引起的像素不一致障碍，我们使用动态注意力图（DAM-GAN）引入了基于GAN的模型。我们提出的大坝甘机集中于检测假纹理和产品动态注意图，以减少发电机中特征图的像素不一致。对Celeba-HQ和Place2数据集的评估结果具有其他图像介入方法，显示了我们网络的优势。
Deep neural advancements have recently brought remarkable image synthesis performance to the field of image inpainting. The adaptation of generative adversarial networks (GAN) in particular has accelerated significant progress in high-quality image reconstruction. However, although many notable GAN-based networks have been proposed for image inpainting, still pixel artifacts or color inconsistency occur in synthesized images during the generation process, which are usually called fake textures. To reduce pixel inconsistency disorder resulted from fake textures, we introduce a GAN-based model using dynamic attention map (DAM-GAN). Our proposed DAM-GAN concentrates on detecting fake texture and products dynamic attention maps to diminish pixel inconsistency from the feature maps in the generator. Evaluation results on CelebA-HQ and Places2 datasets with other image inpainting approaches show the superiority of our network.
