mlsanityData to 7 Oct 2026

A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring

arXiv 2208.04227 ↗First posted 8 Aug 2022cs.LG cs.AIcited by 4

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Abstract (arXiv)

Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classification, multi-label classification in the continual scenario remains a challenging problem. For the first time, we study multi-label classification in the Domain Incremental Learning scenario. Moreover, we propose an efficient approach that has a logarithmic complexity with regard to the number of tasks, and can be applied also in the Class Incremental Learning scenario. We validate our approach on a real-world multi-label Alarm Forecasting problem from the packaging industry. For the sake of reproducibility, the dataset and the code used for the experiments are publicly available.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 8 Aug 2022.

Builds on

18 of 51 references.

References · 18

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  2. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  3. 2016Progressive Neural Networkscited by 1,332
  4. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  5. 2018Experience Replay for Continual Learningcited by 1,074
  6. 2017Focal Loss for Dense Object Detectioncited by 749
  7. 2018Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigencecited by 734
  8. 2013An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networkscited by 711
  9. 2017Lifelong Learning with Dynamically Expandable Networkscited by 689
  10. 2019Three scenarios for continual learningcited by 610
  11. 2016CNN-RNN: A Unified Framework for Multi-label Image Classificationcited by 189
  12. 2019Latent Replay for Real-Time Continual Learningcited by 97
6 more
  1. 2020Rethinking Experience Replay: a Bag of Tricks for Continual Learningcited by 94
  2. 2017The Devil is in the Tails: Fine-grained Classification in the Wildcited by 89
  3. 2020Imbalanced Continual Learning with Partitioning Reservoir Samplingcited by 71
  4. 2018Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learningcited by 33
  5. 2018Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Setscited by 6
  6. 2022Logarithmic Continual Learningcited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 3.

4 papers.

  1. 2022Continual Learning Approaches for Anomaly Detectioncited by 9
  2. 2024Multi-Label Continual Learning for the Medical Domain: A Novel Benchmarkcited by 6
  3. 2024Fairness Evolution in Continual Learning for Medical Imagingcited by 2
  4. 2024Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descentcited by 1