I Detect What I Don’t Know: Incremental Anomaly Learning with SWAG
Authors:
- Nand Kumar YadavNYNand Kumar Yadav
- Rodrigue RizkRRRodrigue Rizk
- William C. W. ChenWCWilliam C. W. Chen
- Kc SantoshKSKc Santosh
- 2 July 2026
- book chapter
- Published by Springer Nature
- p. 129-144
Abstract
No abstract availableThis publication has 13 references indexed in Scilit:
- PATL: Pool-Based Active Twin Learner from Oracle with Imitation Learning for Early Epidemic Detection2025
- TVA-GAN: attention guided generative adversarial network for thermal to visible image transformationsNeural Computing and Applications, 2023
- Active Learning to Minimize the Possible Risk of Future Epidemics2023
- Towards Total Recall in Industrial Anomaly Detection2022
- Covid-19 Imaging Tools: How Big Data is Big?Journal of Medical Systems, 2021
- Continual lifelong learning with neural networks: A reviewNeural Networks, 2019
- f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networksMedical Image Analysis, 2019
- iCaRL: Incremental Classifier and Representation Learning2017
- Overcoming catastrophic forgetting in neural networksProceedings of the National Academy of Sciences, 2017
- Agreeing to disagree: active learning with noisy labels without crowdsourcingInternational Journal of Machine Learning and Cybernetics, 2017