论文标题
轮毂VAE：无监督的基于枢纽的变异自动编码器正规化
Hub-VAE: Unsupervised Hub-based Regularization of Variational Autoencoders
论文作者
论文摘要
基于示例的方法依靠信息性的数据点或原型来指导学习算法的优化。这样的数据促进了可解释的模型设计和预测。特别有趣的是，典范在学习无监督的深度表示方面。在本文中，我们利用枢纽是在高维空间中频繁的邻居，作为范例来正规化变异自动编码器，并学习一个歧视性的嵌入，以实现无处不在的下游任务。我们提出了一个无监督的，数据驱动的潜在空间的正则化，并混合了基于轮毂的先验和基于集线器的对比损失。实验评估表明，与基线和最先进的技术相比，我们的算法在嵌入空间中实现了出色的簇可分离性，以及准确的数据重建和生成。
Exemplar-based methods rely on informative data points or prototypes to guide the optimization of learning algorithms. Such data facilitate interpretable model design and prediction. Of particular interest is the utility of exemplars in learning unsupervised deep representations. In this paper, we leverage hubs, which emerge as frequent neighbors in high-dimensional spaces, as exemplars to regularize a variational autoencoder and to learn a discriminative embedding for unsupervised down-stream tasks. We propose an unsupervised, data-driven regularization of the latent space with a mixture of hub-based priors and a hub-based contrastive loss. Experimental evaluation shows that our algorithm achieves superior cluster separability in the embedding space, and accurate data reconstruction and generation, compared to baselines and state-of-the-art techniques.
