论文标题
基于GMM的生成对抗编码器学习
GMM-Based Generative Adversarial Encoder Learning
论文作者
论文摘要
尽管GAN是生成图像的强大模型，但它无法推断潜在空间直接限制其在需要编码器的应用中的使用。我们的论文提出了一种简单的体系结构设置，将GAN的生成能力与编码器结合在一起。我们通过使用共享权重将编码器与判别器相结合，然后使用新的损失项同时训练它们来实现这一目标。我们通过GMM对编码器潜在空间的输出进行建模，这既可以使用此潜在空间进行良好的聚类，又可以通过GAN改善图像生成。我们的框架是通用的，可以轻松地插入任何GAN策略中。特别是，我们用Vanilla Gan和Wasserstein Gan证明了这一点，在这两种情况下，这两者都可以从IS和FID得分方面改善生成的图像。此外，我们表明我们的编码器学会了有意义的表示形式，因为其聚类结果与当前基于GAN的基于GAN的群集竞争。
While GAN is a powerful model for generating images, its inability to infer a latent space directly limits its use in applications requiring an encoder. Our paper presents a simple architectural setup that combines the generative capabilities of GAN with an encoder. We accomplish this by combining the encoder with the discriminator using shared weights, then training them simultaneously using a new loss term. We model the output of the encoder latent space via a GMM, which leads to both good clustering using this latent space and improved image generation by the GAN. Our framework is generic and can be easily plugged into any GAN strategy. In particular, we demonstrate it both with Vanilla GAN and Wasserstein GAN, where in both it leads to an improvement in the generated images in terms of both the IS and FID scores. Moreover, we show that our encoder learns a meaningful representation as its clustering results are competitive with the current GAN-based state-of-the-art in clustering.
