论文标题
用于断开数据的生成对抗网络的合奏
Ensembles of Generative Adversarial Networks for Disconnected Data
论文作者
论文摘要
大多数当前的计算机视觉数据集由断开的集合组成，例如来自不同类别的图像。我们证明，这种类型的数据的分布无法用连续的生成网络表示没有错误。它们可以通过两种方式表示：通过网络集合或具有截短潜在空间的单个网络。我们表明，在实践中，合奏比截断的分布更为可取。我们构建了一个正规化的优化问题，该问题建立了单个连续gan，gan，有条件的甘恩和高斯混合物gan之间的关系。可以有效地计算此正规化，我们从经验上表明，我们的框架具有性能的最佳位置，可以通过高参数调整找到。与单个连续GAN或CGAN相比，该合奏框架可以更好地性能，同时保持较少的总参数。
Most current computer vision datasets are composed of disconnected sets, such as images from different classes. We prove that distributions of this type of data cannot be represented with a continuous generative network without error. They can be represented in two ways: With an ensemble of networks or with a single network with truncated latent space. We show that ensembles are more desirable than truncated distributions in practice. We construct a regularized optimization problem that establishes the relationship between a single continuous GAN, an ensemble of GANs, conditional GANs, and Gaussian Mixture GANs. This regularization can be computed efficiently, and we show empirically that our framework has a performance sweet spot which can be found with hyperparameter tuning. This ensemble framework allows better performance than a single continuous GAN or cGAN while maintaining fewer total parameters.
