The appearance of generative adversarial networks (GAN) provides a new approach and
framework for computer vision. Compared with traditional machine learning algorithms, GAN …

Spectrogram representations of acoustic scenes have achieved competitive performance for
acoustic scene classification. Yet, the spectrogram alone does not take into account a …

In the area of computer vision, deep learning has produced a variety of state-of-the-art
models that rely on massive labeled data. However, collecting and annotating images from …

Dataset plays an essential role in the training and testing of traffic vision algorithms.
However, the collection and annotation of images from the real world is time-consuming …

Traffic flow prediction is one of the most popular topics in the field of the intelligent
transportation system due to its importance. Powered by advanced machine learning …

Wind power scenarios have a significant impact on stochastic optimization problems for
power systems in which wind power is a significant component. Generative adversarial …

Generative Adversaria Networks (GANs) have emerged as a promising and effective
mechanism for machine learning due to its recent successful applications. GANs share the …

With the increasing demand of security defense., anti-terrorism investigation and disaster
rescue., human activity classification and recognition have become a hot research topic …

In machine learning, generative models are used to create data samples that mimic the
characteristics of the training data. Generative adversarial networks (GANs) are neural …

With the explosive growth of the urban population, an increasing number of cities face the
parking problem. Studies shows that more than 30% of the urban traffic is due to these" …