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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples.

, , , and . ICLR (Poster), OpenReview.net, (2018)

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Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark., , , , , , and . ECCV (20), volume 13680 of Lecture Notes in Computer Science, page 366-382. Springer, (2022)Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless., and . IEEE Access, (2022)On the Equivalence of Linear Discriminant Analysis and Least Squares., and . AAAI, page 2736-2742. AAAI Press, (2015)Robust Inference via Generative Classifiers for Handling Noisy Labels., , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 3763-3772. PMLR, (2019)Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples, , , and . (2017)cite arxiv:1711.09325.Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples., , , and . ICLR (Poster), OpenReview.net, (2018)ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers., , , , , , and . CoRR, (2022)Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference., , , and . CoRR, (2024)On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning., and . CoRR, (2024)Robust Deep Learning in the Open World with Lifelong Learning and Representation Learning.. University of Michigan, USA, (2020)