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Limitations of Assessing Active Learning Performance at Runtime

, , , и . arXiv e-prints, (2019)

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Jaap Denissen

Wladimir Denisow

Alexander Arnold Denisiuk

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Marina Denisova

 

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Toward Optimal Probabilistic Active Learning Using a Bayesian Approach, , , , , и . arXiv e-prints, (2020)dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans., , , и . CoRR, (2024)Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning, , , , , и . arXiv e-prints, (2022)Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension, , , и . arXiv e-prints, (2024)Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets., , , , и . IAL@PKDD/ECML, том 3470 из CEUR Workshop Proceedings, стр. 25-45. CEUR-WS.org, (2023)Role of Hyperparameters in Deep Active Learning., , , и . IAL@PKDD/ECML, том 3470 из CEUR Workshop Proceedings, стр. 19-24. CEUR-WS.org, (2023)A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning., , , , и . IAL@PKDD/ECML, том 3259 из CEUR Workshop Proceedings, стр. 1-6. CEUR-WS.org, (2022)Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks., , , и . ICPR, стр. 9172-9179. IEEE, (2020)A Review of Uncertainty Calibration in Pretrained Object Detectors, , , и . arXiv e-prints, (2022)Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning, , , , и . International Workshop & Tutorial on Interactive Adaptive Learning (IAL), Torino, Italy, (2023)