论文标题
使用可逆层对生成自动编码器的确定性培训
Deterministic training of generative autoencoders using invertible layers
论文作者
论文摘要
在这项工作中，我们为生成自动编码器的随机变分训练提供了确定性的替代方法。我们将这些新的生成自动编码器称为流中的自动编码器（AEF），因为编码器和解码器被定义为整体可逆体系结构的仿射层。与VAE的随机编码相反，这导致了数据的确定性编码。该论文介绍了两个相关的AEF家族。第一个家庭依靠环境空间的分区，并通过精确的最大样子进行训练。第二个家庭利用了环境空间的确定性扩展，并通过最大化该扩展空间的对数概率而受到训练。后一种情况在选择编码器，解码器和先前的体系结构方面留下了完全自由，这使其成为训练现有VAE和VAE风格模型的替换。我们表明，这些AEF的性能比建筑上相同的VAE在对数似然和样品质量方面，尤其是对于低维的潜在空间。重要的是，我们表明AEF样品比VAE样品明显得多。
In this work, we provide a deterministic alternative to the stochastic variational training of generative autoencoders. We refer to these new generative autoencoders as AutoEncoders within Flows (AEF), since the encoder and decoder are defined as affine layers of an overall invertible architecture. This results in a deterministic encoding of the data, as opposed to the stochastic encoding of VAEs. The paper introduces two related families of AEFs. The first family relies on a partition of the ambient space and is trained by exact maximum-likelihood. The second family exploits a deterministic expansion of the ambient space and is trained by maximizing the log-probability in this extended space. This latter case leaves complete freedom in the choice of encoder, decoder and prior architectures, making it a drop-in replacement for the training of existing VAEs and VAE-style models. We show that these AEFs can have strikingly higher performance than architecturally identical VAEs in terms of log-likelihood and sample quality, especially for low dimensional latent spaces. Importantly, we show that AEF samples are substantially sharper than VAE samples.
