  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
