Skip to main content
SpringerLink
Log in

QvQ-IL: quantity versus quality in incremental learning

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The catastrophic forgetting problem is one of the hotspots in the field of deep learning. At present, there is no doubt that storing samples of previous tasks in fixed-size memory is the best way to solve this problem. However, the number of samples stored in fixed-size memory is limited. With the increase in tasks, the number of samples stored in memory for a single task will decrease sharply. It is also difficult to balance the memory capacity and number of samples. To solve this problem, some methods use a fixed size of memory to store dimensionality reduction images. However, this will create new problems. 1) The quality of dimensionality reduction images is poor, and they are significantly different from original images. 2) How to choose the dimensionality reduction method of images. To address these problem, we put forward a new method. Firstly, we employ a simple and reliable scheme to solve the domain difference between dimensionality reduction images and original images. And we theoretically analyzed which image dimensionality reduction method is better. Secondly, to increase the generalization ability of our method and further mitigate the catastrophic forgetting phenomenon, we utilize a self-supervised image augmentation method and the output features similarity loss. Thirdly, we make use of the neural kernel mapping support vector machine theory to improve the interpretability of our method. Experimental results demonstrated that the top-1 average accuracy of our method is much higher than other methods when using the same size of memory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability statements

Dataset(s) derived from public resources and made available with ‘Cheng, G., Han, J., & Lu, X. (2017). Remote Sensing Image Scene Classification: Benchmark and State of the Art. Proceedings of the IEEE, 105(10), 1865–1883. https://doi.org/10.1109/JPROC.2017.2675998.’

References

  1. Marques G, Agarwal D, de la Torre DI (2020) Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network. Appl Soft Comput 96:106691. https://doi.org/10.1016/j.asoc.2020.106691

    Article  Google Scholar 

  2. Zheng Y, Zheng Y, Suehiro D, Uchida S (2021) Top-rank convolutional neural network and its application to medical image-based diagnosis. Pattern Recogn 120:108138. https://doi.org/10.1016/j.patcog.2021.108138

    Article  Google Scholar 

  3. Elemento O, Leslie C, Lundin J, Tourassi G (2021) Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer 21:747–752. https://doi.org/10.1038/s41568-021-00399-1

    Article  Google Scholar 

  4. Dong-Ok W, Klaus-Robert M, Seong-Whan L (2020) An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions. Sci Robot 5:eabb9764. https://doi.org/10.1126/scirobotics.abb9764

    Article  Google Scholar 

  5. Songchen M, Jing P, Weihao Z et al (2022) Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots. Sci Robot 7:eabk2948. https://doi.org/10.1126/scirobotics.abk2948

    Article  Google Scholar 

  6. Mukherjee D, Gupta K, Chang LH, Najjaran H (2022) A survey of robot learning strategies for human-robot collaboration in industrial settings. Robot Comput Integr Manuf 73:102231. https://doi.org/10.1016/j.rcim.2021.102231

    Article  Google Scholar 

  7. Du W, Ding S (2021) A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications. Artif Intell Rev 54:3215–3238. https://doi.org/10.1007/s10462-020-09938-y

    Article  Google Scholar 

  8. Wurman PR, Barrett S, Kawamoto K et al (2022) Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature 602:223–228. https://doi.org/10.1038/s41586-021-04357-7

    Article  Google Scholar 

  9. Silver D, Schrittwieser J, Simonyan K et al (2017) Mastering the game of Go without human knowledge. Nature 550:354–359. https://doi.org/10.1038/nature24270

    Article  Google Scholar 

  10. Yuan J, Zhao Y, Qin B (2022) Learning to share by masking the non-shared for multi-domain sentiment classification. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-022-01556-0

    Article  Google Scholar 

  11. Li Y, Li J, Zhang M (2021) Deep Transformer modeling via grouping skip connection for neural machine translation. Knowl-Based Syst 234:107556. https://doi.org/10.1016/j.knosys.2021.107556

    Article  Google Scholar 

  12. Kumar P, Raman B (2022) A BERT based dual-channel explainable text emotion recognition system. Neural Netw 150:392–407

    Article  Google Scholar 

  13. Zhang H, Li Y, Chen H et al (2022) Memory-efficient hierarchical neural architecture search for image restoration. Int J Comput Vis 130:157–178. https://doi.org/10.1007/s11263-021-01537-w

    Article  Google Scholar 

  14. Chen H, Wei Z, Li X et al (2022) RePCD-Net: feature-aware recurrent point cloud denoising network. Int J Comput Vis 130:615–629. https://doi.org/10.1007/s11263-021-01564-7

    Article  Google Scholar 

  15. Pan J, Sun D, Zhang J et al (2022) Dual convolutional neural networks for low-level vision. Int J Comput Vis 130:1440–1458. https://doi.org/10.1007/s11263-022-01583-y

    Article  Google Scholar 

  16. Mermillod M, Bugaiska A, Bonin P (2013) The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects. Front Psychol 4:1–3. https://doi.org/10.3389/fpsyg.2013.00504

    Article  Google Scholar 

  17. Li Y, Zhang T (2017) Deep neural mapping support vector machines. Neural Netw 93:185–194

    Article  Google Scholar 

  18. Ditzler G, Polikar R (2012) Incremental learning of concept drift from streaming imbalanced data. IEEE Trans Knowl Data Eng 25:2283–2301

    Article  Google Scholar 

  19. Ditzler G, Roveri M, Alippi C, Polikar R (2015) Learning in nonstationary environments: a survey. IEEE Comput Intell Mag 10:12–25

    Article  Google Scholar 

  20. Gama J, Žliobaitė I, Bifet A et al (2014) A survey on concept drift adaptation. ACM Comput Surv 46:1–37

    Article  Google Scholar 

  21. Malialis K, Panayiotou CG, Polycarpou MM (2022) Nonstationary data stream classification with online active learning and siamese neural networks✩. Neurocomputing 512:235–252

    Article  Google Scholar 

  22. Žliobaitė I, Bifet A, Pfahringer B, Holmes G (2013) Active learning with drifting streaming data. IEEE Trans Neural Netw Learn Syst 25:27–39

    Article  Google Scholar 

  23. Yang G, Fini E, Xu D, et al (2022) Continual attentive fusion for incremental learning in semantic segmentation. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2022.3167555

  24. Yu L, Liu X, van de Weijer J (2022) Self-training for class-incremental semantic segmentation. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3155746

  25. Maracani A, Michieli U, Toldo M, Zanuttigh P (2021) RECALL: replay-based continual learning in semantic segmentation. In: 2021 IEEE/CVF international conference on computer vision (ICCV)

  26. Shmelkov K, Schmid C, Alahari K (2017) Incremental learning of object detectors without catastrophic forgetting. In: Proc IEEE int conf comput vis 2017-Oct, pp 3420–3429. https://doi.org/10.1109/ICCV.2017.368

  27. Wang J, Wang X, Shang-Guan Y, Gupta A (2021) Wanderlust: online continual object detection in the real world. In: 2021 IEEE/CVF international conference on computer vision (ICCV)

  28. DONG NA, Zhang Y, Ding M, Lee GH (2021) Bridging non co-occurrence with unlabeled in-the-wild data for incremental object detection. In: Ranzato M, Beygelzimer A, Dauphin Y, et al (eds) Advances in neural information processing systems. Curran Associates, Inc

  29. Huang Y, Zhang Y, Chen J, et al (2021) Continual learning for text classification with information disentanglement based regularization. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies. Association for Computational Linguistics (online)

  30. Monaikul N, Castellucci G, Filice S, Rokhlenko O (2021) Continual learning for named entity recognition. Proc AAAI Confer Artif Intell 35:13570–13577

    Google Scholar 

  31. Sun J, Wang S, Zhang J, Zong C (2020) Distill and replay for continual language learning. In: Proceedings of the 28th international conference on computational linguistics

  32. Tao X, Hong X, Chang X, et al (2020) Few-shot class-incremental learning. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR)

  33. Yoon SW, Kim D-Y, Seo J, Moon J (2020) {X}tar{N}et: learning to extract task-adaptive representation for incremental few-shot learning. In: III HD, Singh A (eds) Proceedings of the 37th international conference on machine learning. PMLR

  34. Zhu K, Cao Y, Zhai W, et al (2021) Self-promoted prototype refinement for few-shot class-incremental learning. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR)

  35. Yan S, Xie J, He X (2021) DER: dynamically expandable representation for class incremental learning. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR)

  36. Qin Q, Hu W, Peng H, et al (2021) BNS: building network structures dynamically for continual learning. In: Ranzato M, Beygelzimer A, Dauphin Y, et al (eds) Advances in neural information processing systems. Curran Associates, Inc

  37. Yoon J, Yang E, Lee J, Hwang SJ (2018) Lifelong learning with dynamically expandable networks. In: 6th International conference on learning representations, pp 1–11

  38. James K, Razvan P, Neil R et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci 114:3521–3526. https://doi.org/10.1073/pnas.1611835114

    Article  MathSciNet  Google Scholar 

  39. Zenke F, Poole B, Ganguli S (2017) Continual learning through synaptic intelligence. In: Precup D, Teh YW (eds) Proceedings of the 34th international conference on machine learning. PMLR

  40. Chaudhry A, Dokania PK, Ajanthan T, Torr PHS (2018) Riemannian walk for incremental learning: understanding forgetting and intransigence. Lect Notes Comput Sci (Includ Subser Lect Notes Artif Intell Lect Notes Bioinformat) 11215 LNCS:556–572. https://doi.org/10.1007/978-3-030-01252-6_33

  41. Rebuffi SA, Kolesnikov A, Sperl G, Lampert CH (2017) iCaRL: incremental classifier and representation learning. In: Proc—30th IEEE conf comput vis pattern recognition, CVPR 2017, pp 5533–5542. https://doi.org/10.1109/CVPR.2017.587

  42. Wu Y, Chen Y, Wang L, et al (2019) Large scale incremental learning. Proc IEEE Comput Soc Confer Comput Vis Pattern Recogn. https://doi.org/10.1109/CVPR.2019.00046

  43. Hou S, Pan X, Loy CC, et al (2019) Learning a unified classifier incrementally via rebalancing. Proc IEEE Comput Soc Conf Comput Vis Pattern Recogn. https://doi.org/10.1109/CVPR.2019.00092

  44. Belouadah E, Popescu A (2019) Il2m: class incremental learning with dual memory. In: Proceedings of the IEEE/CVF international conference on computer vision

  45. Liu Y, Schiele B, Sun Q (2021) RMM: reinforced memory management for class-incremental learning. In: Ranzato M, Beygelzimer A, Dauphin Y, et al (eds) Advances in neural information processing systems. Curran Associates, Inc

  46. Liu Y, Schiele B, Sun Q (2021) Adaptive aggregation networks for class-incremental learning. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR)

  47. Iscen A, Zhang J, Lazebnik S, Schmid C (2020) Memory-efficient incremental learning through feature adaptation BT—computer vision—ECCV 2020. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds). Springer, Cham

  48. Shin H, Lee JK, Kim J, Kim J (2017) Continual learning with deep generative replay. In: Advances in neural information processing systems

  49. Liu X, Wu C, Menta M, et al (2020) Generative feature replay for class-incremental learning. In: 2020 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW)

  50. Zhao H, Wang H, Fu Y et al (2021) Memory efficient class-incremental learning for image classification. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3072041

    Article  Google Scholar 

  51. Wang L, Zhang X, Yang K, et al (2021) Memory replay with data compression for continual learning. In: International conference on learning representations

  52. Hayes TL, Kafle K, Shrestha R, et al (2020) REMIND your neural network to prevent catastrophic forgetting BT—computer vision—ECCV 2020. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds). Springer, Cham

  53. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR)

  54. Komodakis N, Gidaris S (2018) Unsupervised representation learning by predicting image rotations. In: International conference on learning representations (ICLR)

  55. Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Ghahramani Z, Welling M, Cortes C, et al (eds) Advances in neural information processing systems. Curran Associates, Inc

  56. Cheng G, Han J, Lu X (2017) Remote sensing image scene classification: benchmark and state of the art. Proc IEEE 105:1865–1883. https://doi.org/10.1109/JPROC.2017.2675998

    Article  Google Scholar 

  57. Zhou D-W, Ye H-J, Zhan D-C (2021) Co-transport for class-incremental learning. In: Proceedings of the 29th ACM international conference on multimedia. Association for Computing Machinery, New York

  58. Dhar P, Singh R V, Peng K-C, et al (2019) Learning without memorizing. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR)

Download references

Acknowledgements

Our work is supported by the National Natural Science Foundation of China (61806013, 61876010, 61906005), General project of Science and Technology Plan of Beijing Municipal Education Commission (KM202110005028), Project of Interdisciplinary Research Institute of Beijing University of Technology (2021020101) and International Research Cooperation Seed Fund of Beijing University of Technology (2021A01).

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Jidong Han, Ting Zhang, Zhaoying Liu & Yujian Li

  2. School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, 541004, China

    Yujian Li

Authors
  1. Jidong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaoying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yujian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhaoying Liu or Yujian Li.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, J., Zhang, T., Liu, Z. et al. QvQ-IL: quantity versus quality in incremental learning. Neural Comput & Applic 36, 2767–2796 (2024). https://doi.org/10.1007/s00521-023-09129-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09129-0

Keywords

Search

Navigation