47 citations · 103 across the 21 of their papers we have counts for
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cs.LG2023
Adaptive Compression of the Latent Space in Variational Autoencoders
Gabriela Sejnova, Michal Vavrecka, Karla Stepanova
Variational Autoencoders (VAEs) are powerful generative models that have been widely used in various fields, including image and text generation. However, one of the known challeng…
cs.LG2022★ 2 cited
Benchmarking Multimodal Variational Autoencoders: CdSprites+ Dataset and Toolkit
Gabriela Sejnova, Michal Vavrecka, Karla Stepanova +1
Multimodal Variational Autoencoders (VAEs) have been the subject of intense research in the past years as they can integrate multiple modalities into a joint representation and can…