Introducing Fuzzy Layers for Deep Learning

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.

I. Introduction

Artificial intelligence (AI) has emerged over the past decade as one of the most promising technologies for advancing mankind in a multitude of ways, from medicine discovery and disease diagnostics to autonomous vehicles, semantic segmentation, and personal assistants. For many years there has been a desire to create computer algorithms/machines that are able to replace or assist humans in signal understanding for tasks such as automatic buried explosive hazard detection, vehicle navigation, object recognition, and object tracking. AI has grown to loosely encompass a number of state-of-the-art technologies across many fields, e.g., pattern recognition, machine learning (ML), neural networks (NNs), computational intelligence, evolutionary computation, and so on. Recently, much buzz has surrounded deep learning (DL) for its ability to provide desirable results for a number of different applications.