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…
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…
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…
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…