Capsule Networks (CapsNets) are brand-new architectures that have shown ground-breaking results in certain areas of Computer Vision (CV). In 2017, Hinton and his team introduced CapsNets with routing-by-agreement in "Sabour et al" and in a more recent paper "Matrix Capsules with EM Routing" they proposed a more complete architecture with Expectation-Maximization (EM) algorithm. Unlike the traditional convolutional neural networks (CNNs), this architecture is able to preserve the pose of the objects in the picture. Due to this characteristic, it has been able to beat the previous state-of-theart results on the smallNORB dataset, which includes samples with various view points. Also, this architecture is more robust to white box adversarial attacks. However, CapsNets have two major drawbacks. They can't perform as well as CNNs on complex datasets and, they need a huge amount of time for training. We try to mitigate these shortcomings by finding optimum settings of EM routing iterations for training CapsNets. Unlike the past studies, we use un-equal numbers of EM routing iterations for different stages of the CapsNet. For our research, we use three datasets: Yale face dataset, Belgium Traffic Sign dataset, and Fashion-MNIST dataset.
翻译:Capsule Networks(CapsNets) 是一个崭新的架构,在计算机视野(CV)的某些领域显示了突破性成果。 2017年, Hinton及其团队在“ Sabour et al” 和最近的一份论文“ Matrix Capsules with EM Rout” 中引入了CapsNets, 并按路径逐条协议在“ Sabour et al” 和最近一份论文“ Matrix Capsules with EM Rout” 中引入了Capsule Nets 。 与传统的 convolutional 神经网络( CNN) 不同, 这一架构能够保存图片中对象的外观。 由于这个特点, Hintonton及其团队能够击败小NORB 数据集上先前的状态结果, 其中包括各种视图的样本。 另外, 这个架构对白框的对 Emouts 有两个主要的反向。 但是, CapsNet 无法在复杂的数据集上和CNIS 一起运行, 他们需要大量的时间来训练。 我们试图减轻这些缺点。 我们的 Emrout- train lating its face- dreactions flistrutations the dad ad distrudations