2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
Trustworthy Machine Learning
Bálint Mucsányi, Michael Kirchhof, Elisa Nguyen +2
As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalize to small changes in the distributi…
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh
Contrastively trained encoders have recently been proven to invert the data-generating process: they encode each input, e.g., an image, into the true latent vector that generated t…
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning
Michael Kirchhof, Karsten Roth, Zeynep Akata +1
Proxy-based Deep Metric Learning (DML) learns deep representations by embedding images close to their class representatives (proxies), commonly with respect to the angle between th…