3 research outputs found
Cognitive Consistency Routing Algorithm of Capsule-network
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
Grouping Capsules Based Different Types
Capsule network was introduced as a new architecture of neural networks, it
encoding features as capsules to overcome the lacking of equivariant in the
convolutional neural networks. It uses dynamic routing algorithm to train
parameters in different capsule layers, but the dynamic routing algorithm need
to be improved. In this paper, we propose a novel capsule network architecture
and discussed the effect of initialization method of the coupling coefficient
on the model. First, we analyze the rate of change of the initial
value of when the dynamic routing algorithm iterates. The larger the
initial value of , the better effect of the model. Then, we proposed
improvement that training different types of capsules by grouping capsules
based different types. And this improvement can adjust the initial value of
to make it more suitable. We experimented with our improvements on
some computer vision datasets and achieved better results than the original
capsule networ
MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal
Ensemble methods have been widely used for improving the results of the best
single classificationmodel. A large body of works have achieved better
performance mainly by applying one specific ensemble method. However, very few
works have explored complex fusion schemes using het-erogeneous ensembles with
new aggregation strategies. This paper is three-fold: 1) It provides an
overview of the most popular ensemble methods, 2) analyzes several fusion
schemes using MNIST as guiding thread and 3) introduces MNIST-NET10, a complex
heterogeneous fusion architecture based on a degree of certainty aggregation
approach; it combines two heterogeneous schemes from the perspective of data,
model and fusion strategy. MNIST-NET10 reaches a new record in MNISTwith only
10 misclassified images. Our analysis shows that such complex heterogeneous
fusionarchitectures based on the degree of certainty can be considered as a way
of taking benefit fromdiversity