Uncertainty Estimation Using Variational Mixture of Gaussians Capsule Network for Health Image Classification

Table 2

Comparison of the number of parameters generated by each model. The VMG-Routing model produced the least number of parameters with the ML producing the largest number of parameters. () indicates the models that our device could not implement due to memory limitations. The values reported here were thus obtained from the literature. (−) indicates unavailable values. (#c) represents the number of classes in the dataset.

Algorithm	Number of parameters
	CIFAR-10	Fashion-MNIST	MNIST	COVID-19 radiography
VB-routing {64, 8, 16, 16, #c}	145 K	145 K	145 K	120 K
VB-routing {64, 16, 32, 32, #c}	323 K	323 K	—	—
VB-routing {64, 16, 16, 16, #c}	172 K	172 K	—	—
EM-routing {64, 16, 16, 16, #c}	—	323 K	—	—
EM-routing {64, 16, 32, 32, #c}	323 K	—	—	—
Multi-lane LBP-gabor capsule	4.10 M	4.10 M	4.10 M	3.70 M
Dynamic routing	9.3 M	8.2 M	8.2 M	9.8 M
VMG-routing {32, 4, 8, 8, #c} (ours)	14 K	15.5 K	14 K	10.2 K