88 citations · 423 across the 74 of their papers we have counts for
9 papers · 1 filter
Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models
Seyedmorteza Sadat, Otmar Hilliges, Romann M. Weber
Classifier-free guidance (CFG) is crucial for improving both generation quality and alignment between the input condition and final output in diffusion models. While a high guidanc…
No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
Seyedmorteza Sadat, Manuel Kansy, Otmar Hilliges +1
Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an uncondit…
RILe: Reinforced Imitation Learning
Mert Albaba, Sammy Christen, Thomas Langarek +3
Acquiring complex behaviors is essential for artificially intelligent agents, yet learning these behaviors in high-dimensional settings poses a significant challenge due to the vas…
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley +2
Advances in latent diffusion models (LDMs) have revolutionized high-resolution image generation, but the design space of the autoencoder that is central to these systems remains un…
Efficient Learning of High Level Plans from Play
Núria Armengol Urpí, Marco Bagatella, Otmar Hilliges +2
Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goa…
Utilizing Synthetic Data in Supervised Learning for Robust 5-DoF Magnetic Marker Localization
Mengfan Wu, Thomas Langerak, Otmar Hilliges +1
Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems.…
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning
Marco Bagatella, Sammy Christen, Otmar Hilliges
Efficient exploration is a crucial challenge in deep reinforcement learning. Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently a…
CoSE: Compositional Stroke Embeddings
Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi +1
We present a generative model for complex free-form structures such as stroke-based drawing tasks. While previous approaches rely on sequence-based models for drawings of basic obj…
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning
Sammy Christen, Lukas Jendele, Emre Aksan +1
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios. Functional d…