mlsanityData to 14 Oct 2026

A Continual Learning Framework for Adaptive Defect Classification and Inspection

arXiv 2203.08796 ↗First posted 16 Mar 2022cs.CV cs.LG stat.MLcited by 7

Not on the map. Why?

Abstract (arXiv)

Machine-vision-based defect classification techniques have been widely adopted for automatic quality inspection in manufacturing processes. This article describes a general framework for classifying defects from high volume data batches with efficient inspection of unlabelled samples. The concept is to construct a detector to identify new defect types, send them to the inspection station for labelling, and dynamically update the classifier in an efficient manner that reduces both storage and computational needs imposed by data samples of previously observed batches. Both a simulation study on image classification and a case study on surface defect detection via 3D point clouds are performed to demonstrate the effectiveness of the proposed method.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

23 of 42 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  5. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  6. 2016Learning without Forgettingcited by 2,496
  7. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  8. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  9. 2016Progressive Neural Networkscited by 1,361
  10. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  11. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  12. 2017PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruningcited by 898
11 more
  1. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  2. 2019Large Scale Incremental Learningcited by 732
  3. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  4. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  5. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  6. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  7. 2020Incremental Learning In Online Scenariocited by 71
  8. 2020Feature Space Singularity for Out-of-Distribution Detectioncited by 49
  9. 2018Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learningcited by 34
  10. 2020SPlit: An Optimal Method for Data Splittingcited by 15
  11. 2018Bayesian Incremental Learning for Deep Neural Networkscited by 6

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 2.

7 papers.

  1. 2023An Incremental Unified Framework for Small Defect Inspectioncited by 25
  2. 2023Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturingcited by 1
  3. 2023Continual learning for surface defect segmentation by subnetwork creation and selectioncited by 1
  4. 2025Deep Subspace Learning for Surface Anomaly Classification Based on 3D Point Cloud Datacited by 1
  5. 2022Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentationcited by 0
  6. 2024Stream-Based Active Learning for Process Monitoringcited by 0
  7. 2025On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturingcited by 0