Evaluating and explaining generative adversarial networks for continual learning under concept drift
Generative Adversarial Networks (GANs) are among the most popular contemporary
machine learning algorithms. Despite remarkable successes in their developments, existing …
machine learning algorithms. Despite remarkable successes in their developments, existing …
Employing chunk size adaptation to overcome concept drift
Modern analytical systems must be ready to process streaming data and correctly respond
to data distribution changes. The phenomenon of changes in data distributions is called …
to data distribution changes. The phenomenon of changes in data distributions is called …
" From Sketches To Paintings": Image-to-Image Translation using Generative Adversarial Networks/Author Elena Maria Handorfer
EM Handorfer - 2024 - epub.jku.at
Generative adversarial networks (GANs) can approximate an unknown data distribution by
modifying a sampling procedure to generate instances that are more similar to the real data …
modifying a sampling procedure to generate instances that are more similar to the real data …
Exploring Segmentation Models for Chinese Ancient Landscape Paintings
X Lan - 2021 - studenttheses.uu.nl
Semantic segmentation is applied to various tasks such as road images, medical images
and images that need to separate the main objects from the background. With high …
and images that need to separate the main objects from the background. With high …