Gustav Bredell — accepted AI-conference papers

2023

Explicitly Minimizing the Blur Error of Variational Autoencoders

Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil, Ender Konukoglu

ICLR 2023poster

Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on. Significant research effort has been spent to increase the generative capabilities by creating more flexi…

Cited by 30 Source ↗PDF ↗Scholar ↗

2022

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

Metin Ersin Arican, Ozgur Kara, Gustav Bredell, Ender Konukoglu

CVPR 2022poster

Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks in the Deep Image Prior (DIP) framework. The benefit of the inductive bias the network imposes in the DIP framework dep…

Cited by 24 PDF ↗code ↗Scholar ↗