4 papers
The Evolving Nature of Latent Spaces: From GANs to Diffusion
Ludovica Schaerf
This paper examines the evolving nature of internal representations in generative visual models, focusing on the conceptual and technical shift from GANs and VAEs to diffusion-base…
Training-Free Style and Content Transfer by Leveraging U-Net Skip Connections in Stable Diffusion
Ludovica Schaerf, Andrea Alfarano, Fabrizio Silvestri +1
Recent advances in diffusion models for image generation have led to detailed examinations of several components within the U-Net architecture for image editing. While previous stu…
Reflections on Disentanglement and the Latent Space
Ludovica Schaerf
The latent space of image generative models is a multi-dimensional space of compressed hidden visual knowledge. Its entity captivates computer scientists, digital artists, and medi…
Art Forgery Detection using Kolmogorov Arnold and Convolutional Neural Networks
Sandro Boccuzzo, Deborah Desirée Meyer, Ludovica Schaerf
Art authentication has historically established itself as a task requiring profound connoisseurship of one particular artist. Nevertheless, famous art forgers such as Wolfgang Belt…