Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

We propose an alternative generator architecture for generative adversarial networks,
borrowing from style transfer literature. The new architecture leads to an automatically …

Machine learning approaches are becoming increasingly popular to improve the efficiency
of specific emitter identification (SEI). However, in most non-cooperative SEI scenarios …

Deep learning has had tremendous success at learning low-dimensional representations of
high-dimensional data. This success would be impossible if there was no hidden low …

In this study, a data-augmentation method is proposed to narrow the significant difference
between the distribution of training and test sets when small sample sizes are concerned …

VAE requires the standard Gaussian distribution as a prior in the latent space. Since all
codes tend to follow the same prior, it often suffers the so-called" posterior collapse". To …

Achieving a balance between image quality (precision) and diversity (recall) is a significant
challenge in the domain of generative models. Current state-of-the-art models primarily rely …

Image-to-image (I2I) translation has become a key asset for generative adversarial
networks. Convolutional neural networks (CNNs), despite having a significant performance …

Specific emitter identification (SEI) enables the classification of various unique emitters
based on received waveforms using some external feature measurements from their …