论文标题
多线性潜在条件，用于生成看不见的属性组合
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
论文作者
论文摘要
深层生成模型依靠其感应偏置来促进概括，尤其是对于具有高维数据的问题，例如图像。然而，经验研究表明，变异自动编码器（VAE）和生成对抗网络（GAN）缺乏自然在人类感知中自然发生的概括能力。例如，人类只能看到一个微笑的男人，可以想象一个女人微笑。相反，标准条件VAE（CVAE）无法产生看不见的属性组合。为此，我们通过引入一个多线性潜在调节框架来扩展CVAE，该框架捕获属性之间的乘法相互作用。我们实施了模型的两个变体，并在MNIST，Fashion-Mnist和Celeba上展示了它们的功效。总的来说，我们设计了一个新颖的调理框架，可以与任何体系结构一起使用，以合成看不见的属性组合。
Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images. However, empirical studies have shown that variational autoencoders (VAE) and generative adversarial networks (GAN) lack the generalization ability that occurs naturally in human perception. For example, humans can visualize a woman smiling after only seeing a smiling man. On the contrary, the standard conditional VAE (cVAE) is unable to generate unseen attribute combinations. To this end, we extend cVAE by introducing a multilinear latent conditioning framework that captures the multiplicative interactions between the attributes. We implement two variants of our model and demonstrate their efficacy on MNIST, Fashion-MNIST and CelebA. Altogether, we design a novel conditioning framework that can be used with any architecture to synthesize unseen attribute combinations.
