论文标题
道森：一个域自适应的射击生成框架
DAWSON: A Domain Adaptive Few Shot Generation Framework
论文作者
论文摘要
培训生成的对抗网络（GAN）从头开始培训新领域，需要大量的培训数据和培训时间。为此，我们提出了Dawson，Dawson是基于元学习的gans gans的域自适应生成框架。应用元学习gan的一个主要挑战是，由于gan的可能性无可能，在开发集上评估发电机的梯度。为了应对这一挑战，我们提出了一种替代性GAN培训程序，该程序自然结合了gan的两步训练程序和元学习算法的两步训练程序。道森（Dawson）是一个插件框架，它支持广泛的元学习算法和各种植物的建筑变化剂。基于道森（Dawson），我们还提出了音乐节目，这是第一批音乐生成模型。我们的实验表明，音乐节目可以迅速适应新的领域，只有数十首来自目标域中的歌曲。我们还表明，道森可以学会在MNIST数据集中只有四个样本生成新数字。我们在Pytorch和Tensorflow中发布了Dawson的源代码实现，并在两个流派和闪电视频上生成了音乐样本。
Training a Generative Adversarial Networks (GAN) for a new domain from scratch requires an enormous amount of training data and days of training time. To this end, we propose DAWSON, a Domain Adaptive FewShot Generation FrameworkFor GANs based on meta-learning. A major challenge of applying meta-learning GANs is to obtain gradients for the generator from evaluating it on development sets due to the likelihood-free nature of GANs. To address this challenge, we propose an alternative GAN training procedure that naturally combines the two-step training procedure of GANs and the two-step training procedure of meta-learning algorithms. DAWSON is a plug-and-play framework that supports a broad family of meta-learning algorithms and various GANs with architectural-variants. Based on DAWSON, We also propose MUSIC MATINEE, which is the first few-shot music generation model. Our experiments show that MUSIC MATINEE could quickly adapt to new domains with only tens of songs from the target domains. We also show that DAWSON can learn to generate new digits with only four samples in the MNIST dataset. We release source codes implementation of DAWSON in both PyTorch and Tensorflow, generated music samples on two genres and the lightning video.
