论文标题
无监督的少量图像生成的增强插值自动编码器
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation
论文作者
论文摘要
我们旨在建立图像生成模型，从而从几个示例中推广到新域。为此，我们首先研究了经典图像发生器的概括属性，并发现自动编码器即使对高度受约束的数据进行培训，自动编码器也可以很好地推广到新的域。我们利用这种见解来产生一种强大的，无监督的少数图像生成算法，并基于从数据增强中恢复图像的新型培训程序。我们的增强间隔自动编码器仅从几个参考图像中综合了新颖对象的现实图像，并且表现优于先前的插值模型和监督的少量图像发生器。我们的程序简单且轻巧，广泛概括，并且在培训期间不需要类别标签或其他监督。
We aim to build image generation models that generalize to new domains from few examples. To this end, we first investigate the generalization properties of classic image generators, and discover that autoencoders generalize extremely well to new domains, even when trained on highly constrained data. We leverage this insight to produce a robust, unsupervised few-shot image generation algorithm, and introduce a novel training procedure based on recovering an image from data augmentations. Our Augmentation-Interpolative AutoEncoders synthesize realistic images of novel objects from only a few reference images, and outperform both prior interpolative models and supervised few-shot image generators. Our procedure is simple and lightweight, generalizes broadly, and requires no category labels or other supervision during training.
