1 citations · 2 across the 11 of their papers we have counts for
3 papers · 1 filter
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…
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…
DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
Erik Wallin, Lennart Svensson, Fredrik Kahl +1
Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular. SSL is a family of methods, which in addition to a labeled traini…