论文标题
独立矢量变分自动编码器的半监督分解器
Semi-supervised Disentanglement with Independent Vector Variational Autoencoders
论文作者
论文摘要
我们的目标是将数据的生成因子分为变异自动编码器中的两个潜在向量。一个向量捕获了与目标分类任务相关的班级因素，而另一个向量捕获了与其余信息相关的样式因素。为了学习离散的类功能，我们使用少量标记的数据介绍了监督，这些数据可以简单地有效地减少在现有的无监督方法中执行的超参数调谐所需的精力。此外，我们引入了一个学习目标，以鼓励向量之间的统计独立性。我们表明（i）在分解与多个潜在媒介的证据下获得的结果之内，该矢量独立项存在，并且（ii）鼓励这种独立性以及降低向量内的总相关性，从而增强了分解性能。在几个图像数据集上进行的实验表明，通过我们的方法实现的分离可以提高分类性能和发电可控性。
We aim to separate the generative factors of data into two latent vectors in a variational autoencoder. One vector captures class factors relevant to target classification tasks, while the other vector captures style factors relevant to the remaining information. To learn the discrete class features, we introduce supervision using a small amount of labeled data, which can simply yet effectively reduce the effort required for hyperparameter tuning performed in existing unsupervised methods. Furthermore, we introduce a learning objective to encourage statistical independence between the vectors. We show that (i) this vector independence term exists within the result obtained on decomposing the evidence lower bound with multiple latent vectors, and (ii) encouraging such independence along with reducing the total correlation within the vectors enhances disentanglement performance. Experiments conducted on several image datasets demonstrate that the disentanglement achieved via our method can improve classification performance and generation controllability.
