Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

In machine learning, a generative model is responsible for generating new samples of data
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …

Despite the significant progress that has been made in the training of Generative Adversarial
Networks (GANs), the mode collapse problem remains a major challenge in training GANs …

Generative adversarial network (GAN) has been regarded as a promising solution to many
machine learning problems, and it comprises of a generator and discriminator, determining …

Adversarial attacks have threatened the credibility of machine learning models and cast
doubts over the integrity of data. The attacks have created much harm in the fields of …

Conditional generative adversarial networks (cGANs) aim to synthesize diverse images
given the input conditions and the latent codes, but they are prone to map an input to a …

The oil and gas industry employs numerical simulation tools extensively in reservoir analysis
and strategic planning. This study presents a machine-learning proxy model, employing a …

Predicting compound–protein interactions (CPIs) is of great importance for drug discovery
and repositioning, yet still challenging mainly due to the sparse nature of CPI matrixes …