In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a Teacher- Network we are able to improve the quality of the generated images as well as perform weakly object localization on the generated images. To this end, we generate images of HEp-2 cells captured with Indirect Imunofluoresence (IIF) and study the ability of our network to perform a weakly localization of the cell. Firstly, we demonstrate that whilst GANs can learn the mapping between the input domain and the target distribution efficiently, the discriminator network is not able to detect the regions of interest. Secondly, we present a novel attention transfer mechanism which allows us to enforce the discriminator to put emphasis on the regions of interest via transfer learning. Thirdly, we show that this leads to more realistic images, as the discriminator learns to put emphasis on the area of interest. Fourthly, the proposed method allows one to generate both images as well as attention maps which can be useful for data annotation e.g in object detection.

本研究提出了一种基于生成对抗网络的训练方法，通过利用教师-网络生成的注意力图来提高图像生成的质量，以及在生成的图像上执行弱目标定位。该方法在 HEp-2 细胞图像上进行了研究，表明其可以进行弱定位。该方法可用于图像生成和数据注释，例如在目标检测中。

关注力感知生成式对抗网络 (ATA-GANs)