Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications in recent years. However, mode collapse remains a critical problem in GANs. In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs. Different from existing methods, we propose to generalize the discriminator as feature embedding and maximize the entropy of distributions in the embedding space learned by the discriminator. Specifically, two regularization terms, i.e., Deep Local Linear Embedding (DLLE) and Deep Isometric feature Mapping (DIsoMap), are designed to encourage the discriminator to learn the structural information embedded in the data, such that the embedding space learned by the discriminator can be well-formed. Based on the well-learned embedding space supported by the discriminator, a non-parametric entropy estimator is designed to efficiently maximize the entropy of embedding vectors, playing as an approximation of maximizing the entropy of the generated distribution. By improving the discriminator and maximizing the distance of the most similar samples in the embedding space, our pipeline effectively reduces the mode collapse without sacrificing the quality of generated samples. Extensive experimental results show the effectiveness of our method, which outperforms the GAN baseline, MaF-GAN on CelebA (9.13 vs. 12.43 in FID) and surpasses the recent state-of-the-art energy-based model on the ANIME-FACE dataset (2.80 vs. 2.26 in Inception score). The code is available at https://github.com/HaozheLiu-ST/MEE

翻译：生成对抗网络（GANs）近年来在各类任务与应用中展现出显著成效，但模式坍缩仍是其关键缺陷。本文提出一种新型训练流程以解决GANs的模式坍缩问题。与现有方法不同，我们提出将判别器泛化为特征嵌入，并最大化判别器所学习嵌入空间中分布的熵。具体而言，我们设计了两种正则化项——深度局部线性嵌入（DLLE）与深度等距特征映射（DIsoMap），以促使判别器学习数据内在的结构信息，从而形成良好的嵌入空间。基于判别器支撑的良好嵌入空间，我们设计了一种非参数熵估计器，通过高效最大化嵌入向量的熵，近似实现生成分布熵的最大化。通过改进判别器并最大化嵌入空间中相似样本的距离，本流程在不牺牲生成样本质量的前提下有效缓解了模式坍缩。大量实验证明本方法有效性：在FID指标上，本方法在CelebA数据集上显著优于GAN基线模型MaF-GAN（9.13 vs. 12.43），并在ANIME-FACE数据集上以Inception分数（2.80 vs. 2.26）超越最新基于能量的模型。代码开源地址：https://github.com/HaozheLiu-ST/MEE