Deep capsule networks have more capsule layers, which makes their performance better on complex images. However, with the increase of layers, overfitting will become more serious. Image reconstruction is an effective regularization method for capsule networks. To improve it, we propose an adversarial decoder that introduces the generative adversarial network framework into the reconstruction process to implement learnable reconstruction losses. This architecture consists of three parts: a deep capsule network, a decoder, and a discriminator. The deep capsule network extracts feature capsules from input images, which are then reconstructed by the decoder. The discriminator is the learnable reconstruction loss function that evaluates the similarity between reconstructed images and input images. Minimizing this learnable reconstruction loss and mean square error of images provides a regularization effect for the deep capsule network. Experimental results show that our models have a competitive performance of regularization on CIFAR10, CIFAR100, and FMNIST.

中文翻译：

---

基于生成对抗网络框架的深度胶囊网络正则化

深度胶囊网络有更多的胶囊层，这使得它们在复杂图像上的性能更好。但是随着层数的增加，过拟合会越来越严重。图像重建是胶囊网络的有效正则化方法。为了改进它，我们提出了一种对抗性解码器，将生成对抗性网络框架引入到重建过程中，以实现可学习的重建损失。该架构由三部分组成：深度胶囊网络、解码器和鉴别器。深度胶囊网络从输入图像中提取特征胶囊，然后由解码器重建。鉴别器是可学习的重建损失函数，用于评估重建图像和输入图像之间的相似性。最小化这种可学习的重建损失和图像的均方误差为深度胶囊网络提供了正则化效果。实验结果表明，我们的模型在 CIFAR10、CIFAR100 和 FMNIST 上的正则化性能具有竞争力。