论文标题
变性胶囊编码器
Variational Capsule Encoder
论文作者
论文摘要
我们提出了一种基于新型胶囊网络的新型编码器结构，称为贝叶斯胶囊（B-CAPS），以调节潜在空间中采样分布的平均值和标准偏差。我们假设这种方法可以比传统方法学习潜在空间中特征的更好代表。我们的假设是通过将学习的潜在变量用于图像重建任务来检验的，在这种情况下，对于MNIST和时尚 - 纳斯特数据集，使用我们建议的模型在潜在空间中成功分离了不同的类别。我们的实验结果表明，这两个数据集的重建和分类性能得到了改善，为我们的假设增添了可信度。我们还表明，通过增加潜在空间维度，与传统的变分自动编码器（VAE）相比，提出的B-CAP能够学习更好的表示。因此，我们的结果表明在表示学习中胶囊网络的强度，而这之前从未在VAE设置下进行过研究。
We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space. We hypothesized that this approach can learn a better representation of features in the latent space than traditional approaches. Our hypothesis was tested by using the learned latent variables for image reconstruction task, where for MNIST and Fashion-MNIST datasets, different classes were separated successfully in the latent space using our proposed model. Our experimental results have shown improved reconstruction and classification performances for both datasets adding credence to our hypothesis. We also showed that by increasing the latent space dimension, the proposed B-Caps was able to learn a better representation when compared to the traditional variational auto-encoders (VAE). Hence our results indicate the strength of capsule networks in representation learning which has never been examined under the VAE settings before.
