A Deep Neuro-Fuzzy Network for Image Classification
This work addresses the need for improved neuro-fuzzy systems in image classification, but it appears incremental as it extends existing concepts to a deep architecture without claiming major breakthroughs.
The authors tackled the problem of low generalization capacity in shallow neuro-fuzzy systems by proposing the first end-to-end deep neuro-fuzzy network for image classification, achieving reasonable accuracy on MNIST, CIFAR-10, and CIFAR-100 datasets.
The combination of neural network and fuzzy systems into neuro-fuzzy systems integrates fuzzy reasoning rules into the connectionist networks. However, the existing neuro-fuzzy systems are developed under shallow structures having lower generalization capacity. We propose the first end-to-end deep neuro-fuzzy network and investigate its application for image classification. Two new operations are developed based on definitions of Takagi-Sugeno-Kang (TSK) fuzzy model namely fuzzy inference operation and fuzzy pooling operations; stacks of these operations comprise the layers in this network. We evaluate the network on MNIST, CIFAR-10 and CIFAR-100 datasets, finding that the network has a reasonable accuracy in these benchmarks.