4 citations · 9 across the 6 of their papers we have counts for
3 papers · 1 filter
In Search of Forgotten Domain Generalization
Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer +4
Out-of-Domain (OOD) generalization is the ability of a model trained on one or more domains to generalize to unseen domains. In the ImageNet era of computer vision, evaluation sets…
Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak +2
Foundation models like CLIP are trained on hundreds of millions of samples and effortlessly generalize to new tasks and inputs. Out of the box, CLIP shows stellar zero-shot and few…
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
Jörg Wagner, Jan Mathias Köhler, Tobias Gindele +3
To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a pos…