10 citations · 31 across the 5 of their papers we have counts for
6 papers
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks
Alexander Kovalenko, Pavel Kordík, Magda Friedjungová
Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks, where now excessively large models are…
Image Inpainting Using Wasserstein Generative Adversarial Imputation Network
Daniel Vašata, Tomáš Halama, Magda Friedjungová
Image inpainting is one of the important tasks in computer vision which focuses on the reconstruction of missing regions in an image. The aim of this paper is to introduce an image…
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Magda Friedjungová, Daniel Vašata, Maksym Balatsko +1
Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction.…
Unsupervised Latent Space Translation Network
Magda Friedjungová, Daniel Vašata, Tomáš Chobola +1
One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Cur…
Constructing a Data Visualization Recommender System
Petra Kubernátová, Magda Friedjungová, Max van Duijn
Choosing a suitable visualization for data is a difficult task. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such…
Missing Features Reconstruction and Its Impact on Classification Accuracy
Magda Friedjungová, Daniel Vašata, Marcel Jiřina
In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values…