论文标题
通过$α$ -Divergence桥接最大的可能性和对抗性学习
Bridging Maximum Likelihood and Adversarial Learning via $α$-Divergence
论文作者
论文摘要
最大似然（ML）和对抗性学习是培训生成模型的两种流行方法，从许多角度来看，这些技术是互补的。 ML学习鼓励捕获所有数据模式，通常以稳定的培训来表征。但是，ML学习倾向于在数据空间上分布概率质量，例如$ $，产生模糊的合成图像。尽管有模式下降和精致的训练，但众所周知，众所周知，尽管有实用的挑战，但众所周知，可以综合高度逼真的自然图像。我们提出了一个$α$桥，以统一ML和对抗性学习的优势，从而通过$α$ divergence平稳地转移到另一个。我们透露，$α$桥的概括与最近开发的方法密切相关，以使对抗性学习正常，提供对先前工作的见解，并进一步了解$α$桥在实践中表现良好的原因。
Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary. ML learning encourages the capture of all data modes, and it is typically characterized by stable training. However, ML learning tends to distribute probability mass diffusely over the data space, $e.g.$, yielding blurry synthetic images. Adversarial learning is well known to synthesize highly realistic natural images, despite practical challenges like mode dropping and delicate training. We propose an $α$-Bridge to unify the advantages of ML and adversarial learning, enabling the smooth transfer from one to the other via the $α$-divergence. We reveal that generalizations of the $α$-Bridge are closely related to approaches developed recently to regularize adversarial learning, providing insights into that prior work, and further understanding of why the $α$-Bridge performs well in practice.
