This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial
autoencoders, and their variants. We start with explaining adversarial learning and the …

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral,
probabilistic, and deep metric learning. We first start with the definition of distance metric …

Accurate fatigue assessment of material plagued by defects is of utmost importance to
guarantee safety and service continuity in engineering components. This study shows how …

In infrared scenes, humans can easily observe objects in the scene with their eyes, even dim
ones. To make the robot have the same visual ability, this article proposes a pyramid-feature …

The neural process (NP) is a family of computationally efficient models for learning
distributions over functions. However, it suffers from under-fitting and shows suboptimal …

Modern industrial processes are characteristic of nonstationary and uncertainty. To address
these issues, this paper proposes a probabilistic principal component analysis based model …

Emotion is an interdisciplinary research field investigated by many research areas such as
psychology, philosophy, computing, and others. Emotions influence how we make …

Improving the ergonomy of working environments is essential to reducing work-related
musculo-skeletal disorders. We consider real-time ergonomic feedback a key technology for …

Among deep generative models, variational autoencoders (VAEs) are a central approach in
generating new samples from a learned, latent space while effectively reconstructing input …

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction
method with a probabilistic approach. In SNE, every point is consider to be the neighbor of …