论文标题
MMCGAN：具有显式流形的生成对抗网络先验
MMCGAN: Generative Adversarial Network with Explicit Manifold Prior
论文作者
论文摘要
生成的对抗网络（GAN）提供了一个良好的生成框架来生成逼真的样本，但在模式崩溃和不稳定的培训中遭受了两个公认的问题。在这项工作中，我们建议在减轻模式崩溃和稳定GAN训练之前采用明确的多种学习学习。由于传统流形学习的基本假设在稀疏和不均匀的数据分布的情况下失败了，因此我们引入了一个新的目标，最小的歧管编码（MMC），以鼓励流形学习以鼓励简单而展开的歧管。从本质上讲，MMC是最短的汉密尔顿路径问题的一般情况，并以最少的riemann量追求歧管。使用MMC的标准化代码作为先验，保证GAN可以恢复一个涵盖所有培训数据的简单而展开的歧管。我们对玩具数据和真实数据集的实验表明，MMCGAN在减轻模式下崩溃，稳定训练以及提高生成样品的质量的有效性。
Generative Adversarial Network(GAN) provides a good generative framework to produce realistic samples, but suffers from two recognized issues as mode collapse and unstable training. In this work, we propose to employ explicit manifold learning as prior to alleviate mode collapse and stabilize training of GAN. Since the basic assumption of conventional manifold learning fails in case of sparse and uneven data distribution, we introduce a new target, Minimum Manifold Coding (MMC), for manifold learning to encourage simple and unfolded manifold. In essence, MMC is the general case of the shortest Hamiltonian Path problem and pursues manifold with minimum Riemann volume. Using the standardized code from MMC as prior, GAN is guaranteed to recover a simple and unfolded manifold covering all the training data. Our experiments on both the toy data and real datasets show the effectiveness of MMCGAN in alleviating mode collapse, stabilizing training, and improving the quality of generated samples.
