For the text‐to‐image synthesis task, most discriminators in existing generative adversarial networks based methods tend to fall into a local suboptimal state too early in the training process, resulting in the poor quality of generated images. To address the above problems, a hybrid augmented loss discriminator is designed. In this designed discriminator, to reduce the sensitivity of the discriminator classification recognition, make it pay attention to the semantic and structural changes, we add the loss value of the fake sample to the loss value of the real sample. Moreover, to indirectly guide the generator to generate samples, the loss value of the real sample is added to the fake sample. The loss value mixed with real and fake samples actually augments signal transmission. It perturbs parameter update of the discriminator during optimization and prevents the discriminator from falling into the local suboptimal state prematurely. Whereafter, we apply the proposed discriminator to two kinds of text‐to‐image synthesis tasks. Experimental results show that the proposed method can help the baseline models to improve performance.

中文翻译：

---

一种用于文本到图像合成的新型混合增强损失鉴别器

对于文本到图像的合成任务，现有的基于生成对抗网络的方法中的大多数鉴别器往往在训练过程中过早地陷入局部次优状态，导致生成图像的质量很差。为了解决上述问题，设计了一种混合增强损失鉴别器。在这个设计的判别器中，为了降低判别器分类识别的敏感性，使其关注语义和结构的变化，我们将假样本的损失值加到真实样本的损失值上。而且，为了间接引导生成器生成样本，将真实样本的损失值加入到假样本中。与真假样本混合的损失值实际上增强了信号传输。它在优化过程中扰乱鉴别器的参数更新，防止鉴别器过早陷入局部次优状态。此后，我们将提出的鉴别器应用于两种文本到图像的合成任务。实验结果表明，所提出的方法可以帮助基线模型提高性能。