论文标题
带有各种熵正规化器的甘恩：缓解模式爆发问题的应用
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue
论文作者
论文摘要
基于深度学习的成功，生成的对抗网络（GAN）提供了一种现代方法，可以从观察到的样本中学习概率分布。甘恩通常被配制为两组功能之间的零和游戏。发电机和歧视器。尽管甘斯在学习复杂分布（例如图像）方面表现出巨大的潜力，但它们通常会遭受发电机未能捕获输入分布的所有现有模式的模式崩溃问题。结果，生成的样品的多样性低于观察到的样品。为了解决这个问题，我们采用了一种信息理论方法，并最大程度地提高了生成样品熵的变异下限，以提高其多样性。我们将这种方法称为具有各种熵正规化器（GAN+VER）的gan。 gan中模式崩溃问题的现有补救措施可以很容易地与我们提出的变分熵正规化结合在一起。通过对标准基准数据集进行的广泛实验，我们显示了所有现有的评估指标突出了GAN+VER的所有现有评估指标，这些指标突出了实际样品和生成样品的差异。
Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs are often formulated as a zero-sum game between two sets of functions; the generator and the discriminator. Although GANs have shown great potentials in learning complex distributions such as images, they often suffer from the mode collapse issue where the generator fails to capture all existing modes of the input distribution. As a consequence, the diversity of generated samples is lower than that of the observed ones. To tackle this issue, we take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity. We call this approach GANs with Variational Entropy Regularizers (GAN+VER). Existing remedies for the mode collapse issue in GANs can be easily coupled with our proposed variational entropy regularization. Through extensive experimentation on standard benchmark datasets, we show all the existing evaluation metrics highlighting difference of real and generated samples are significantly improved with GAN+VER.
