Interval Type-2 Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

Deep learning (DL) has played a crucial role in many domains of image and pattern recognition, extraction of features from video and text processing etc. One of the quintessential elements of deep learning is Restricted Boltzmann Machines (RBM). RBMs are capable of extracting the high-level features from raw data very efficiently. Nevertheless, feature extraction process is prone to external and unwanted noises, which introduces uncertainty in the decision making process. Moreover, existing RBM-based DL methods are not robust enough to handle such noises in the data samples while training within layers. To tackle these drawbacks, Fuzzy Restricted Boltzmann Machine (FRBM) had been available in the literature. FRBM utilizes Type-1 Fuzzy Sets (T1FS) to handle such uncertainties in governing parameters of the system. However, membership values of membership functions used in T1FSs are also crisp. Thus, in this state-of-art paper, we propose the use Interval Type-2 Fuzzy Sets (IT2FSs) to model parameters in RBM for training, as they are efficient in handling higher level of uncertainty. Experiments performed for MNIST digits show more generative and discriminative capabilities of IT2FRBM over RBM and FRBM.