论文标题
随机凸多属的几何分离
Geometric Disentanglement by Random Convex Polytopes
论文作者
论文摘要
我们提出了一种新的几何方法，用于衡量从深度学习中获得的表示的质量。我们的方法（称为随机多层描述符）根据随机凸多型的构建提供了有效描述数据点。我们通过定性比较经典和正规自动编码器的行为来证明我们的技术的使用。这表明将正则化应用于自动编码器网络可能会降低潜在空间中的分布外检测性能。尽管我们的技术在精神上与$ k $ - 均值聚类相似，但我们在自动编码数据集上的聚类任务中实现了更好的假阳性/负平衡。
We propose a new geometric method for measuring the quality of representations obtained from deep learning. Our approach, called Random Polytope Descriptor, provides an efficient description of data points based on the construction of random convex polytopes. We demonstrate the use of our technique by qualitatively comparing the behavior of classic and regularized autoencoders. This reveals that applying regularization to autoencoder networks may decrease the out-of-distribution detection performance in latent space. While our technique is similar in spirit to $k$-means clustering, we achieve significantly better false positive/negative balance in clustering tasks on autoencoded datasets.
