论文标题
基于属性的潜在空间正规化变量自动编码器
Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders
论文作者
论文摘要
使用深层生成模型对数据属性进行选择性操纵是一个活跃的研究领域。在本文中，我们提出了一种新颖的方法，用于构建变异自动编码器（VAE）的潜在空间，以明确编码不同的连续值属性。这是通过使用属性正则化损失来实现的，该属性损失可以在属性值与要编码属性的维度的潜在代码之间实现单调关系。因此，训练后，该模型可用于通过简单地更改相应正规化维度的潜在代码来操纵属性。从几个定量和定性实验中获得的结果表明，所提出的方法导致分离且可解释的潜在空间，可用于有效地操纵跨越图像和符号音乐域的广泛数据属性。
Selective manipulation of data attributes using deep generative models is an active area of research. In this paper, we present a novel method to structure the latent space of a Variational Auto-Encoder (VAE) to encode different continuous-valued attributes explicitly. This is accomplished by using an attribute regularization loss which enforces a monotonic relationship between the attribute values and the latent code of the dimension along which the attribute is to be encoded. Consequently, post-training, the model can be used to manipulate the attribute by simply changing the latent code of the corresponding regularized dimension. The results obtained from several quantitative and qualitative experiments show that the proposed method leads to disentangled and interpretable latent spaces that can be used to effectively manipulate a wide range of data attributes spanning image and symbolic music domains.
