Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Neural networks are used to learn task-oriented high-level representations in an end-to-end
manner by building a multi-layer neural network. Generation models have developed rapidly …

espite their value in promoting open discourse, social media plat-forms are often exploited to
spread harmful content. Current deep learning and natural language processing models …

Although deep neural networks have achieved amazing results on instance segmentation,
they are still ill-equipped when they are required to learn new tasks incrementally …

The development of deep learning based image representation learning (IRL) methods has
attracted great attention for various image understanding problems. Most of these methods …

Good representations can help RL agents perform concise modeling of their surroundings,
and thus support effective decision-making in complex environments. Previous methods …

We propose a novel semi-supervised learning method of Variational AutoEncoder (VAE),
which yields a customized latent space through our EXplainable encoder Network (EXoN) …

Although a multimodal data analysis, comprising physiological and questionnaire survey
data, provides better insights into addressing management science concerns, such as …

We address the problem of web supervised learning, in particular for face attribute
classification. Web data suffers from image set noise, due to unrelated images that may be …

We present the development of a semi-supervised regression method using variational
autoencoders (VAE) for soft sensing of process quality variables. Recently, use of VAEs was …