Experimental Analysis and Complexity Scaling of Denoising Diffusion Probabilistic Models for Image Synthesis
Abstract: This talk will present an experimental study on the behaviour, scalability, and generative quality of Denoising Diffusion Probabilistic Models (DDPMs) for image synthesis across datasets of increasing complexity. Three case studies are analysed and compared based on FID (Fréchet Inception Distance) evaluation metric and the performance of downstream classifiers to discriminate the synthetically generated … Read more








