Instance Selection for GANs

Recent advances in Generative Adversarial Networks (GANs) have led to their widespread adoption for the purposes of generating high quality synthetic imagery. While capable of generating photo-realistic images, these models often produce unrealistic samples which fall outside of the data manifold.Several recently proposed techniques attempt to avoid spurious samples, either by rejecting them after generation, or by truncating the model's latent space. While effective, these methods are inefficient, as a large fraction of training time and model capacity are dedicated towards samples that will ultimately go unused. In this work we propose a novel approach to improve sample quality: altering the training dataset via instance selection before model training has taken place. By refining the empirical data distribution before training, we redirect model capacity towards high-density regions, which ultimately improves sample fidelity, lowers model capacity requirements, and significantly reduces training time. Code is available at https://github.com/uoguelph-mlrg/instance_selection_for_gans.

GAN的实例选择

生成对抗网络（GAN）的最新进展已使其广泛用于生成高质量合成图像的目的。这些模型虽然能够生成逼真的图像，但通常会产生不真实的样本，这些样本落在数据流形之外。.. 最近提出的几种技术试图通过在生成后将其拒绝或将模型的潜在空间截断来避免虚假样本。这些方法虽然有效，但效率不高，因为培训时间和模型能力的很大一部分专用于最终将不使用的样本。在这项工作中，我们提出了一种提高样本质量的新颖方法：在进行模型训练之前，通过实例选择来更改训练数据集。通过优化训练前的经验数据分布，我们将模型容量重定向到高密度区域，从而最终提高了样本保真度，降低了模型容量要求，并显着减少了训练时间。可以从https://github.com/uoguelph-mlrg/instance_selection_for_gans获得代码。 （阅读更多）