Generative adversarial networks (GANs) have been extensively studied in the past few
years. Arguably their most significant impact has been in the area of computer vision where …

Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

Recent years have witnessed the rapid progress of generative adversarial networks (GANs).
However, the success of the GAN models hinges on a large amount of training data. This …

Abstract Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model predictions. In …

Classical machine learning implicitly assumes that labels of the training data are sampled
from a clean distribution, which can be too restrictive for real-world scenarios. However …

The fault characteristics of the rolling bearings of wind turbine gearboxes are unstable under
actual operating conditions. Problems such as inadequate fault sample data, imbalanced …

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for
academic and industry researchers and graduate students taking courses on machine …

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical
simulation is developed, systematically integrated with mixed reality (MR), 5G cloud …

Energy disaggregation, namely the separation of the aggregated household energy
consumption signal into its additive sub-components, bears resemblance to the signal …