1 citations · 2 across the 5 of their papers we have counts for
5 papers
QDSB: Quantized Diffusion Schrödinger Bridges
Tobias Fuchs, Florian Kalinke, Nadja Klein
Learning generative models in settings where the source and target distributions are only specified through unpaired samples is gaining in importance. Here, one frequently-used mod…
Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation
Tobias Fuchs, Nadja Klein
Real-world data is frequently noisy and ambiguous. In crowdsourcing, for example, human annotators may assign conflicting class labels to the same instances. Partial-label learning…
Robust Partial-Label Learning by Leveraging Class Activation Values
Tobias Fuchs, Florian Kalinke
Real-world training data is often noisy; for example, human annotators assign conflicting class labels to the same instances. Partial-label learning (PLL) is a weakly supervised le…
Partial-Label Learning with Conformal Candidate Cleaning
Tobias Fuchs, Florian Kalinke
Real-world data is often ambiguous; for example, human annotation produces instances with multiple conflicting class labels. Partial-label learning (PLL) aims at training a classif…
Partial-Label Learning with a Reject Option
Tobias Fuchs, Florian Kalinke, Klemens Böhm
In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classi…