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Information × Registration Number 2123U011184, Article popup.category Препринт Title Mixup and Metric Learning in Out-of-Distribution Detection (AI translated) popup.author Hutor OleksandraHutor Oleksandra popup.publication 01-01-2023 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4435 popup.publisher Description In this work, we analyse the problem of out-of-distribution detection, which includes inlier classification and outlier detection, and the use of two methods for it, i.e., Mixup and metric learning, as well as their combination. Inspired by the use of Mixup between inliers and outliers in MixOE (Zhang et al., 2023), our first objective is to identify the key ingredients of their method and to combine it with seven other Mixup techniques. We also change the size and diversity of the auxiliary outlier dataset, which is used for training. We find that in the fine-grained OoD setting, where outliers come from the same domain as inliers, mixing only the label with the one that comes from the uniform probability distribution without mixing the inlier and outlier training samples has a similar performance as mixing both pairs of training samples and labels. At the same time, in the coarse-grained settings, where outliers come from a completely different domain than inliers, the more outliers are used for mixing, the better the detection performance is. Our second objective is the investigation of metric learning in the form of triplet loss for the same setup with different types of triplet combinations, some of which are created using Mixup. In the coarse-grained settings, the triplet combination with only inlier classes performs better than such combinations with outliers, mixed or not. Finally, we combine MixOE and our metric learning approach to show that, for some datasets, the detection performance in the coarse-grained settings is comparably larger than the previous best result, MixOE. popup.nrat_date 2025-11-05 Close