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cs.LG2023
OOD Aware Supervised Contrastive Learning
Soroush Seifi, Daniel Olmeda Reino, Nikolay Chumerin +1
Out-of-Distribution (OOD) detection is a crucial problem for the safe deployment of machine learning models identifying samples that fall outside of the training distribution, i.e.…
cs.LG2020
Identifying Wrongly Predicted Samples: A Method for Active Learning
Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino
State-of-the-art machine learning models require access to significant amount of annotated data in order to achieve the desired level of performance. While unlabelled data can be l…