论文标题
使用扩散变化自动编码器与超球潜在空间的分离
Disentanglement with Hyperspherical Latent Spaces using Diffusion Variational Autoencoders
论文作者
论文摘要
数据集的分离表示应能够恢复产生的数据集的基本因素。出现的一个问题是，在潜在的生成因子具有一定的几何结构时，将欧几里得空间用于潜在变量模型是否会产生分离的表示。以不同角度看到的汽车图像为例。该角度具有周期性结构，但一维表示将无法捕获此拓扑。我们如何解决这个问题？ Neurips2019脱离挑战的第一阶段提出的提交挑战由扩散变化自动编码器（$δ$ vae）组成，并具有超球潜在空间，例如，可以恢复周期性的真实因素。 $δ$ vae的训练可以通过合并证据下限（ELBO）的修改版本来调整后近似值的编码能力来增强。
A disentangled representation of a data set should be capable of recovering the underlying factors that generated it. One question that arises is whether using Euclidean space for latent variable models can produce a disentangled representation when the underlying generating factors have a certain geometrical structure. Take for example the images of a car seen from different angles. The angle has a periodic structure but a 1-dimensional representation would fail to capture this topology. How can we address this problem? The submissions presented for the first stage of the NeurIPS2019 Disentanglement Challenge consist of a Diffusion Variational Autoencoder ($Δ$VAE) with a hyperspherical latent space which can, for example, recover periodic true factors. The training of the $Δ$VAE is enhanced by incorporating a modified version of the Evidence Lower Bound (ELBO) for tailoring the encoding capacity of the posterior approximate.
