4 papers
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.…
Contrastive Classification and Representation Learning with Probabilistic Interpretation
Rahaf Aljundi, Yash Patel, Milan Sulc +2
Cross entropy loss has served as the main objective function for classification-based tasks. Widely deployed for learning neural network classifiers, it shows both effectiveness an…
Continual Novelty Detection
Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin +1
Novelty Detection methods identify samples that are not representative of a model's training set thereby flagging misleading predictions and bringing a greater flexibility and tran…
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…