论文标题
用于群集的双向对手自动编码器
Dual Adversarial Auto-Encoders for Clustering
论文作者
论文摘要
作为探索性数据分析的有力方法，无监督的聚类是计算机视觉和模式识别的基本任务。已经开发了许多聚类算法，但是大多数算法对具有复杂结构的数据不满意。最近，对抗性自动编码器（AAE）通过组合自动编码器（AE）和对抗性训练可以显示出对解决此类数据的有效性，但它无法从未标记的数据中有效提取分类信息。在这项工作中，我们提出了双重对手自动编码器（双aae），该自动编码器同时最大程度地提高了观察到的示例和潜在变量的子集之间的可能性函数和相互信息。通过对双ae的目标函数进行各种推断，我们得出了一种新的重建损失，可以通过训练一对自动编码器来优化。此外，为避免模式崩溃，我们为类别变量引入了聚类正则化项。四个基准测试的实验表明，双AAE比最先进的聚类方法实现了卓越的性能。此外，通过添加拒绝选项，双AAE的聚类准确性可以达到受监督的CNN算法的聚类精度。双aae也可以用于删除图像的样式和内容，而无需使用监督信息。
As a powerful approach for exploratory data analysis, unsupervised clustering is a fundamental task in computer vision and pattern recognition. Many clustering algorithms have been developed, but most of them perform unsatisfactorily on the data with complex structures. Recently, Adversarial Auto-Encoder (AAE) shows effectiveness on tackling such data by combining Auto-Encoder (AE) and adversarial training, but it cannot effectively extract classification information from the unlabeled data. In this work, we propose Dual Adversarial Auto-encoder (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information between observed examples and a subset of latent variables. By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders. Moreover, to avoid mode collapse, we introduce the clustering regularization term for the category variable. Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods. Besides, by adding a reject option, the clustering accuracy of Dual-AAE can reach that of supervised CNN algorithms. Dual-AAE can also be used for disentangling style and content of images without using supervised information.
