论文标题
软核：分析和改善内省变化自动编码器
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
论文作者
论文摘要
最近引入的内省变分自动编码器（Introvae）表现出出色的图像世代，并允许使用图像编码器摊销推断。 Introvae的主要思想是使用VAE编码来区分生成的数据样本和真实数据样本，以对抗进行vae。但是，原始的静脉损失函数依赖于特定的铰链损失配方，在实践中很难稳定，其理论收敛分析忽略了损失中的重要术语。在这项工作中，我们朝着更好地理解Interovae模型，其实际实施及其应用迈出了一步。我们提出了软内旋转，这是一种修饰的静脉内流式，以对产生的样品的平滑指数损失代替铰链损失项。这种变化显着提高了训练稳定性，还可以对完整算法进行理论分析。有趣的是，我们表明，静脉内流收敛到最小化数据分布和熵项的KL距离之和的分布。我们讨论了该结果的含义，并证明它诱导了竞争性图像的产生和重建。最后，我们描述了软内部对无监督图像翻译和分布外检测的两种应用，并证明了令人信服的结果。代码和其他信息可在项目网站上找到-https://taldatech.github.io/soft-intro-vae-web
The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main idea in IntroVAE is to train a VAE adversarially, using the VAE encoder to discriminate between generated and real data samples. However, the original IntroVAE loss function relied on a particular hinge-loss formulation that is very hard to stabilize in practice, and its theoretical convergence analysis ignored important terms in the loss. In this work, we take a step towards better understanding of the IntroVAE model, its practical implementation, and its applications. We propose the Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a smooth exponential loss on generated samples. This change significantly improves training stability, and also enables theoretical analysis of the complete algorithm. Interestingly, we show that the IntroVAE converges to a distribution that minimizes a sum of KL distance from the data distribution and an entropy term. We discuss the implications of this result, and demonstrate that it induces competitive image generation and reconstruction. Finally, we describe two applications of Soft-IntroVAE to unsupervised image translation and out-of-distribution detection, and demonstrate compelling results. Code and additional information is available on the project website -- https://taldatech.github.io/soft-intro-vae-web
