Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

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 …

Generative adversarial networks (GAN) represent two deep learning (DL) models positioned
in an adversarial manner to generate and evaluate images. This area of research promises …

Text-guided image inpainting aims to generate corrupted image patches and obtain a
plausible image based on textual descriptions, considering the relationship between textual …

Automatic colorization of anime line art videos aims to produce color frames given line art
frames and reference color images, which is challenging due to various motions and …

Federated learning achieves joint training of deep models by connecting decentralized
datasources, which can significantly mitigate the risk of privacy leakage. However, in a more …

Making reliable recovery of missing traffic data facilitates diverse applications of data-driven
intelligent transportation system. But faced with correlation and heterogeneity along spatial …

For autoregressive image generation, vector-quantized VAEs (VQ-VAEs) quantize image
features with discrete codebook entries and reconstruct images from quantized features …

In graph neural network applications, GraphSAGE applies inductive learning and has been
widely applied in important research topics such as node classification. The subgraph of …

The use of natural phenomena as inspiration to address real-life problems has become an
increasingly popular research approach. In the medical domain, generative adversarial …