While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation.

中文翻译：

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使用传输VAE有条件地为未配对的数据进行分布外生成

尽管生成模型在以低维描述符（MNIST的笔触粗细，CelebA的发色，WaveNet的说话者身份）为条件的高维样本采样中显示出了巨大的成功，但由于存在难度，生成分布不合理会带来基本问题跨条件学习紧凑的联合分布。例如，条件变分自动编码器（CVAE）的规范示例在训练过程中没有明确关联条件，因此没有明确动机来学习这种紧凑表示形式。