title: Mode Regularized Generative Adversarial Networks
abstract: Although Generative Adversarial Networks achieve state-of-the-art results on a
  variety of generative tasks, they are regarded as highly unstable and prone to miss
  modes. We argue that these bad behaviors of GANs are due to the very particular
  functional shape of the trained discriminators in high dimensional spaces, which
  can easily make training stuck or push probability mass in the wrong direction,
  towards that of higher concentration than that of the data generating distribution.
  We introduce several ways of regularizing the objective, which can dramatically
  stabilize the training of GAN models. We also show that our regularizers can help
  the fair distribution of probability mass across the modes of the data generating
  distribution during the early phases of training, thus providing a unified solution
  to the missing modes problem.
authors: Tong Che Yanran Li Athul Jacob Yoshua Bengio Wenjie Li
hIndex: Yoshua Bengio 183