论文标题
使用重要的加权来控制半监管变异自动编码器中的发电和推理之间的相互作用
Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance Weighting
论文作者
论文摘要
即使各种自动编码器（VAE）广泛用于半监督学习，但它们的工作原因仍不清楚。实际上，添加无监督的目标通常被模糊地描述为正规化。这种正则化的强度是通过对训练集未标记部分的目标下降来控制的。通过对半监督VAE的目标的分析，我们观察到他们使用学习的生成模型的后部来指导推理模型，以学习部分观察到的潜在变量。我们表明，鉴于这种观察，可以更好地控制无监督目标对训练程序的影响。使用重要的加权，我们得出了两个新的目标，它们优先考虑部分观察到的潜在变量之一或未观察到的潜在变量。 IMDB英语情感分析数据集和AG新闻主题分类数据集的实验显示了我们的优先级机制带来的改进，并展示了一种与我们对半居住的VAE内部工作的描述有关的行为。
Even though Variational Autoencoders (VAEs) are widely used for semi-supervised learning, the reason why they work remains unclear. In fact, the addition of the unsupervised objective is most often vaguely described as a regularization. The strength of this regularization is controlled by down-weighting the objective on the unlabeled part of the training set. Through an analysis of the objective of semi-supervised VAEs, we observe that they use the posterior of the learned generative model to guide the inference model in learning the partially observed latent variable. We show that given this observation, it is possible to gain finer control on the effect of the unsupervised objective on the training procedure. Using importance weighting, we derive two novel objectives that prioritize either one of the partially observed latent variable, or the unobserved latent variable. Experiments on the IMDB english sentiment analysis dataset and on the AG News topic classification dataset show the improvements brought by our prioritization mechanism and exhibit a behavior that is inline with our description of the inner working of Semi-Supervised VAEs.
