Producing diverse and realistic images with generative models such as GANs typically requires large scale training with vast amount of images. GANs trained with limited data can easily memorize few training samples and display undesirable properties like "stairlike" latent space where interpolation in the latent space yields discontinuous transitions in the output space. In this work, we consider a challenging task of pretraining-free few-shot image synthesis, and seek to train existing generative models with minimal overfitting and mode collapse. We propose mixup-based distance regularization on the feature space of both a generator and the counterpart discriminator that encourages the two players to reason not only about the scarce observed data points but the relative distances in the feature space they reside. Qualitative and quantitative evaluation on diverse datasets demonstrates that our method is generally applicable to existing models to enhance both fidelity and diversity under few-shot setting. Code is available.
翻译:以基因模型(如GANs)产生多样化和现实的图像,通常需要用大量图像进行大规模培训。经过有限数据培训的GANs可以很容易地将为数不多的培训样本混为一谈,并展示出潜在空间的内插产生产出空间不连续性转变的“丝绸”潜在空间等不可取的特性。在这项工作中,我们认为培训前无短片图像合成是一项具有挑战性的任务,并寻求对现有的基因模型进行培训,使其具有最小的超度和模式崩溃。我们提议对发电机和对应的区别器的特征空间进行基于混合的距离规范,鼓励这两个角色不仅考虑所观测到的稀少的数据点,而且考虑它们所居住的特征空间的相对距离。对各种数据集的定性和定量评估表明,我们的方法通常适用于现有的模型,以便在少发的设置下加强真实性和多样性。我们有可用的代码。