6 papers
Representation-Conditioned Diffusion Models for Guided Training Data Generation
Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel Eilertsen
Data availability remains a critical bottleneck in many deep learning applications. Large-scale datasets are often expensive to collect, curate and annotate, which can limit the sc…
Towards Controllable Image Generation through Representation-Conditioned Diffusion Models
Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel Eilertsen
Diffusion models have emerged as powerful tools for high-quality image generation and editing, but guiding these models to produce specific outputs remains a challenge. Conventiona…
Commutator-Induced Uncertainty in VAEs
Tahereh Dehdarirad, Michael Felsberg, Gabriel Eilertsen +1
Variational autoencoders (VAEs) often struggle to represent non-commutative structure in learned latent spaces. Symmetry-aware VAEs commonly address this issue by enforcing commuta…
Enhancing Out-of-Distribution Detection with Extended Logit Normalization
Yifan Ding, Xixi Liu, Jonas Unger +1
\noindent Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning models. Extensive work has focused on devising various scoring functions for…
AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results
Chao Wang, Francesco Banterle, Bin Ren +25
This paper presents a comprehensive review of the AIM 2025 Challenge on Inverse Tone Mapping (ITM). The challenge aimed to push forward the development of effective ITM algorithms…
Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
Yifan Ding, Arturas Aleksandraus, Amirhossein Ahmadian +3
Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning systems, particularly in safety-critical applications. Likelihood-based deep generativ…