Cognitive Consistency Routing Algorithm of Capsule-network
This work addresses a domain-specific issue for researchers in neural network design, but it is incremental as it builds on existing CapsNet methods.
The paper tackles the problem of improving the routing algorithm in Capsule Neural Networks by incorporating psychological theories of Cognitive Consistency to better mimic human brain patterns, resulting in experimental progress compared to the baseline.
Artificial Neural Networks (ANNs) are computational models inspired by the central nervous system (especially the brain) of animals and are used to estimate or generate unknown approximation functions relied on large amounts of inputs. Capsule Neural Network (Sabour S, et al.[2017]) is a novel structure of Convolutional Neural Networks which simulates the visual processing system of human brain. In this paper, we introduce psychological theories which called Cognitive Consistency to optimize the routing algorithm of Capsnet to make it more close to the work pattern of human brain. It has been shown in the experiment that a progress had been made compared with the baseline.