先进的非迭代路由算法：EfficientCapsNet引入了一种基于自注意机制的非迭代路由算法，这种算法能有效应对后续层胶囊数量减少的问题，从而在保证效果的同时大幅减少了参数数量。高效的胶囊表示能力：胶囊网络本身具有更好的概括能力，EfficientCapsNet通过优化架构和算法，使得即使在参数大幅减少的情况下，也能嵌入更...
不过，近日，来自意大利的研究者提出了一种高效的自注意路由胶囊网络（Capsule Network with Self-Attention Routing, Efficient-CapsNet)：他们深入研究了胶囊网络的的效率，并在参数仅仅有16万的情况下，将网络的性能推到了极致。在实验部分，研究者证明，他们提出的架构，在参数量降低为原始CAPSNET的2%的情况下，还可...
不过,近日,来自意大利的研究者提出了一种高效的自注意路由胶囊网络(Capsule Network with Self-Attention Routing, Efficient-CapsNet): 他们深入研究了胶囊网络的的效率,并在参数仅仅有16万的情况下,将网络的性能推到了极致。 在实验部分,研究者证明,他们提出的架构,在参数量降低为原始CAPSNET的2%的情况下,还可以在...
our baseline is identical to Sabour et al.10with the exception of a reduced number of feature maps and layers, in order to keep the number of parameters as close as possible to Efficient-CapsNet. On the other hand, “Base-CapsNet” is a CNN but with a vectorial output...
Using three public datasets, JAFFE, CK+, and FER2013, we comprehensively compared the recognition accuracy and training efficiency of Efficient-CapsNet and CapsNet. Results showed that the Efficient-CapsNet's recognition accuracy reached 99.13%, 93.07%, and 72.94%, respectively, which is superior to...
open source the code (most of) developed during our "first-stage" research on capsules, summarized by the forthcoming article "Efficient-CapsNet: Capsule Network with Self-Attention Routing". The repository let you play with Efficient-CapsNet and let you set the base for your own experiments. ...
Efficient-CapsNet的总体架构如下图所示: 图:Efficient-CapsNet的总体架构示意图 主胶囊利用深度可分卷积,来创建它们所代表的特征的向量表示。另一方面,卷积层的第一个堆栈将输入张量映射到一个高维空间,从而促进了胶囊的创建。 该网络可以被分为三个不同的部分,其中前两个主要实现了胶囊层和输入空间之间的交互。每个...
Efficient-CapsNet的总体架构如下图所示: 图:Efficient-CapsNet的总体架构示意图 主胶囊利用深度可分卷积,来创建它们所代表的特征的向量表示。另一方面,卷积层的第一个堆栈将输入张量映射到一个高维空间,从而促进了胶囊的创建。 该网络可以被分为三个不同的部分,其中前两个主要实现了胶囊层和输入空间之间的交互。每个...
CNN's limitations can be overcome through CapsNet via encode and decode operations. However, CapsNets are emerging for medical image categorization and need complexity reduction optimizations. We present Time Efficient-CapsNet (TE-CapsNet), a unique model, to classify diseases accurately with low ...
We explore how the routing-by-agreement algorithms proposed by the CapsNet, a new convolution neural network structure that deals with pose invariance of objects, is able to find the normalized coordinates of the (single) object on the canvas. These coordinates serve as features to learn affine ...