23 citations · 42 across the 13 of their papers we have counts for
4 papers · 1 filter
Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency
Michael Kirchhof, James Thornton, Louis Béthune +3
The adoption of text-to-image diffusion models raises concerns over reliability, drawing scrutiny under the lens of various metrics like calibration, fairness, or compute efficienc…
Uncertainties of Latent Representations in Computer Vision
Michael Kirchhof
Uncertainty quantification is a key pillar of trustworthy machine learning. It enables safe reactions under unsafe inputs, like predicting only when the machine learning model dete…
Pretrained Visual Uncertainties
Michael Kirchhof, Mark Collier, Seong Joon Oh +1
Accurate uncertainty estimation is vital to trustworthy machine learning, yet uncertainties typically have to be learned for each task anew. This work introduces the first pretrain…
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty qua…