mlsanityData to 14 Oct 2026

Continual Evidential Deep Learning for Out-of-Distribution Detection

arXiv 2309.02995 ↗First posted 6 Sep 2023cs.CVcited by 11

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Abstract (arXiv)

Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 50 of its 65 references that are in the map. First posted on arXiv 6 Sep 2023.

Builds on

50 of 65 references.

References · 50

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38 more
  1. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  2. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  3. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  4. 2017Continual Learning with Deep Generative Replaycited by 1,158
  5. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  6. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  7. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  8. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  9. 2018Overcoming catastrophic forgetting with hard attention to the taskcited by 681
  10. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
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  15. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  16. 2017Variational Continual Learningcited by 339
  17. 2019Maintaining Discrimination and Fairness in Class Incremental Learningcited by 283
  18. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 280
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  20. 2021Online Continual Learning in Image Classification: An Empirical Surveycited by 245
  21. 2020Uncertainty Quantification and Deep Ensemblescited by 139
  22. 2018Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgettingcited by 127
  23. 2019Uncertainty-guided Continual Learning with Bayesian Neural Networkscited by 102
  24. 2021Evidential Deep Learning for Open Set Action Recognitioncited by 96
  25. 2022A Theoretical Study on Solving Continual Learningcited by 80
  26. 2018Memory Replay GANs: learning to generate images from new categories without forgettingcited by 77
  27. 2020Generalized Variational Continual Learningcited by 56
  28. 2023Learnability and Algorithm for Continual Learningcited by 28
  29. 2022A Multi-Head Model for Continual Learning via Out-of-Distribution Replaycited by 27
  30. 2022Continual Learning Based on OOD Detection and Task Maskingcited by 21
  31. 2023What Can Human Sketches Do for Object Detection?cited by 19
  32. 2020Multidimensional Uncertainty-Aware Evidential Neural Networkscited by 16
  33. 2022Back to the Basics: Revisiting Out-of-Distribution Detection Baselinescited by 16
  34. 2019Quantifying Classification Uncertainty using Regularized Evidential Neural Networkscited by 13
  35. 2021Continual Novelty Detectioncited by 10
  36. 2022Out-Of-Distribution Detection In Unsupervised Continual Learningcited by 10
  37. 2022Evidential Deep Learning for Class-Incremental Semantic Segmentationcited by 6
  38. 2020Very simple statistical evidence that AlphaGo has exceeded human limits in playing GO gamecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 3.

11 papers.

  1. 2024Revisiting Essential and Nonessential Settings of Evidential Deep Learningcited by 15
  2. 2024Uncertainty Estimation for 3D Object Detection via Evidential Learningcited by 5
  3. 2025H2ST: Hierarchical Two-Sample Tests for Continual Out-of-Distribution Detectioncited by 4
  4. 2025Label Calibration in Source Free Domain Adaptationcited by 3
  5. 2025Buffer-free Class-Incremental Learning with Out-of-Distribution Detectioncited by 2
  6. 2024Uncertainty Quantification in Continual Open-World Learningcited by 1
  7. 2025Reliable Imputed-Sample Assisted Vertical Federated Learningcited by 0
  8. 2025Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Weldingcited by 0
  9. 2026BD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learningcited by 0
  10. 2026Beyond a Single Signal: SPECTREG2, A Unified MultiExpert Anomaly Detector for Unknown Unknownscited by 0
  11. 2026Rethinking Vacuity for OOD Detection in Evidential Deep Learningcited by 0