4 papers
Semi-Supervised Hierarchical Open-Set Classification
Erik Wallin, Fredrik Kahl, Lars Hammarstrand
Hierarchical open-set classification handles previously unseen classes by assigning them to the most appropriate high-level category in a class taxonomy. We extend this paradigm to…
ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection
Erik Wallin, Lennart Svensson, Fredrik Kahl +1
In open-set semi-supervised learning (OSSL), we consider unlabeled datasets that may contain unknown classes. Existing OSSL methods often use the softmax confidence for classifying…
Exploring Semi-Supervised Learning for Online Mapping
Adam Lilja, Erik Wallin, Junsheng Fu +1
The ability to generate online maps using only onboard sensory information is crucial for enabling autonomous driving beyond well-mapped areas. Training models for this task -- pre…
ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks
Erik Wallin, Fredrik Kahl, Lars Hammarstrand
Out-of-distribution (OOD) detection in deep learning has traditionally been framed as a binary task, where samples are either classified as belonging to the known classes or marked…