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PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare

arXiv 2403.08197 ↗First posted 13 Mar 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

We propose PAGE, a domain-incremental adaptation strategy with past-agnostic generative replay for smart healthcare. PAGE enables generative replay without the aid of any preserved data or information from prior domains. When adapting to a new domain, it exploits real data from the new distribution and the current model to generate synthetic data that retain the learned knowledge of previous domains. By replaying the synthetic data with the new real data during training, PAGE achieves a good balance between domain adaptation and knowledge retention. In addition, we incorporate an extended inductive conformal prediction (EICP) method into PAGE to produce a confidence score and a credibility value for each detection result. This makes the predictions interpretable and provides statistical guarantees for disease detection in smart healthcare applications. We demonstrate PAGE's effectiveness in domain-incremental disease detection with three distinct disease datasets collected from commercially available WMSs. PAGE achieves highly competitive performance against state-of-the-art with superior scalability, data privacy, and feasibility. Furthermore, PAGE can enable up to 75% reduction in clinical workload with the help of EICP.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 52 references that are in the map. First posted on arXiv 13 Mar 2024.

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References · 27

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  2. 2016Learning without Forgettingcited by 2,465
  3. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  4. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  5. 2017Continual Learning with Deep Generative Replaycited by 1,152
  6. 2021Learning to Prompt for Continual Learningcited by 938
  7. 2017PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruningcited by 882
  8. 2019Large Scale Incremental Learningcited by 727
  9. 2017Lifelong Learning with Dynamically Expandable Networkscited by 689
  10. 2019Three scenarios for continual learningcited by 610
  11. 2020A Brief Review of Domain Adaptationcited by 287
  12. 2021Online Continual Learning in Image Classification: An Empirical Surveycited by 242
15 more
  1. 2019REMIND Your Neural Network to Prevent Catastrophic Forgettingcited by 197
  2. 2017Domain Adaptation for Visual Applications: A Comprehensive Surveycited by 127
  3. 2020Optimal Continual Learning has Perfect Memory and is NP-hardcited by 86
  4. 2021Recent Advances of Continual Learning in Computer Vision: An Overviewcited by 55
  5. 2020SpaceNet: Make Free Space For Continual Learningcited by 53
  6. 2021Continual Learning for Recurrent Neural Networks: an Empirical Evaluationcited by 34
  7. 2022How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Compositioncited by 27
  8. 2022Online Continual Learning with Contrastive Vision Transformercited by 21
  9. 2021Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applicationscited by 17
  10. 2022CTRL: Clustering Training Losses for Label Error Detectioncited by 17
  11. 2019DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networkscited by 7
  12. 2020CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networkscited by 7
  13. 2023DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensorscited by 7
  14. 2021Towards continual task learning in artificial neural networks: current approaches and insights from neurosciencecited by 6
  15. 2020TUTOR: Training Neural Networks Using Decision Rules as Model Priorscited by 5

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