arXiv:2512.01152v5 Announce Type: replace-cross
Abstract: As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts....
By Shravan Chaudhari, Yoav Wald, Suchi Saria
arXiv:2607. 12094v1 Announce Type: cross Abstract: Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models.
By Ayush Karmacharya (Purdue University), Luke Luschwitz (Purdue University), Lucia Romero (Purdue University), Yanan Niu (EPFL), Joseph Campbell (Purdue University)
arXiv:2303. 18031v2 Announce Type: replace-cross Abstract: In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the data distribution differs between the training and inference phases.
By Masashi Noguchi, Shinichi Shirakawa
arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.
By Anastasios Romanos Varvarigos, Nikos Giakoumoglou, Tania Stathaki
arXiv:2606. 19712v1 Announce Type: new Abstract: While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can guide efficient model selection.
By Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman, Michael Coss, Shubham Jain
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal is to correctly recognize known classes while d...