1 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Activation Subspaces for Out-of-Distribution Detection
Barış Zöngür, Robin Hesse, Stefan Roth
To ensure the reliability of deep models in real-world applications, out-of-distribution (OOD) detection methods aim to distinguish samples close to the training distribution (in-d…
Disentangling Polysemantic Channels in Convolutional Neural Networks
Robin Hesse, Jonas Fischer, Simone Schaub-Meyer +1
Mechanistic interpretability is concerned with analyzing individual components in a (convolutional) neural network (CNN) and how they form larger circuits representing decision mec…
Beyond Accuracy: What Matters in Designing Well-Behaved Image Classification Models?
Robin Hesse, Doğukan Bağcı, Bernt Schiele +2
Deep learning has become an essential part of computer vision, with deep neural networks (DNNs) excelling in predictive performance. However, they often fall short in other critica…
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…