TY - THES A1 - Rampas, Dominic Antonio T1 - On neural compression using diffusion autoencoders N2 - This work presents an examination of using diffusion models to achieve efficient data compression. As diffusion models have become more dominant in areas such as generative modelling, this work shows that they achieve a strong performance on the task of image-, and video-compression and outperform classical algorithms, as well as established neural compression algorithms. This type of model, referred to as Diffusion Autoencoders, is able to achieve spatial compression factors of 32x, while achieving high quality reconstructions that even maintain fine details of the input. The bachelor thesis presents a thorough analysis of individual components of the diffusion autoencoder through extensive experiments, while undermining the solidness of the approach by numerous comparisons and ablation studies. Additionally, an in-depth overview of related work for classical-, and neural-compression, diffusion models and evaluation methods is given. Y1 - 2024 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-44286 CY - Ingolstadt ER - TY - CHAP A1 - Pernias, Pablo A1 - Rampas, Dominic A1 - Richter, Mats Leon A1 - Pal, Christopher A1 - Aubreville, Marc T1 - Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models T2 - The Twelfth International Conference on Learning Representations (ICLR 2024) Y1 - 2024 UR - https://openreview.net/forum?id=gU58d5QeGv PB - OpenReview ER -