Skip to main content
SpringerLink
Log in

Continually trained life-long classification

  • S.I. : WSOM 2019
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Two challenges can be found in a life-long classifier that learns continually: the concept drift, when the probability distribution of data is changing in time, and catastrophic forgetting when the earlier learned knowledge is lost. There are many proposed solutions to each challenge, but very little research is done to solve both challenges simultaneously. We show that both the concept drift and catastrophic forgetting are closely related to our proposed description of the life-long continual classification. We describe the process of continual learning as a wrap modification, where a wrap is a manifold that can be trained to cover or uncover a given set of samples. The notion of wraps and their cover/uncover modifiers are theoretical building blocks of a novel general life-long learning scheme, implemented as an ensemble of variational autoencoders. The proposed algorithm is examined on evaluation scenarios for continual learning and compared to state-of-the-art algorithms demonstrating the robustness to catastrophic forgetting and adaptability to concept drift but also showing the new challenges of the life-long classification.

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

Similar content being viewed by others

Notes

  1. Implementation of the evaluation framework and the ensgendel algorithm is available at https://github.com/comrob/ensgendel.

  2. https://github.com/giparisi/GDM.

  3. https://github.com/fikavw/CloGAN

  4. Non-intuitively, the famous Peano space-filling curve shows that there exists a continuous function that maps a continuous curve to a two-dimensional square that can be generalized to the n-dimensional cubes.

References

  1. Achille A, Eccles T, Matthey L, Burgess CP, Watters N, Lerchner A, Higgins I (2018) Life-long disentangled representation learning with cross-domain latent homologies. In: International conference on neural information processing systems, pp 9895–9905

  2. Borghesi A, Bartolini A, Lombardi M, Milano M, Benini L (2019) Anomaly detection using autoencoders in high performance computing systems, pp 9428–9433 . https://doi.org/10.1609/aaai.v33i01.33019428

  3. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):15:1–15:58 . https://doi.org/10.1145/1541880.1541882

  4. Elwell R, Polikar R (2011) Incremental learning of concept drift in nonstationary environments. IEEE Trans Neural Netw 22(10):1517–1531. https://doi.org/10.1109/TNN.2011.2160459

    Article  Google Scholar 

  5. French RM (1999) Catastrophic forgetting in connectionist networks. Trends Cogn Sci 3(4):128–135. https://doi.org/10.1016/S1364-6613(99)01294-2

    Article  Google Scholar 

  6. Ja Gama, Žliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv. https://doi.org/10.1145/2523813

    Article  MATH  Google Scholar 

  7. Gepperth A, Hammer B (2016) Incremental learning algorithms and applications. In: European symposium on artificial neural networks (ESANN), pp 357–368

  8. Hinton GE, McClelland JL, Rumelhart DE (1986) Distributed representations. In: Rumelhart DE, McClelland JL, C. PDP Research Group (eds) Parallel Distributed processing: explorations in the microstructure of cognition, vol 1: foundations. MIT Press, Cambridge, pp 77–109

  9. Kemker R, Kanan C (2018) Fearnet: brain-inspired model for incremental learning. In: International conference on learning representations (ICLR)

  10. Kemker R, McClure M, Abitino A, Hayes TL, Kanan C (2018) Measuring catastrophic forgetting in neural networks. In: McIlraith SA, Weinberger KQ (eds) AAAI conference on artificial intelligence, pp 3390–3398

  11. Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd international conference on learning representations, ICLR, San Diego, CA, USA, May 7–9, conference track proceedings (2015). arxiv: 1412.6980

  12. Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: 2nd International conference on learning representations, ICLR, Banff, AB, Canada, conference track proceedings. arxiv: 1312.6114

  13. Kirkpatrick J et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Nat Acad Sci 114(13):3521–3526. https://doi.org/10.1073/pnas.1611835114

    Article  MathSciNet  MATH  Google Scholar 

  14. Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37(2):233–243. https://doi.org/10.1002/aic.690370209

    Article  Google Scholar 

  15. Krawczyk B, Minku LL, Gama J, Stefanowski J, Woźniak M (2017) Ensemble learning for data stream analysis: a survey. Inform Fusion 37:132–156. https://doi.org/10.1016/j.inffus.2017.02.004

    Article  Google Scholar 

  16. Krawczyk B, Woźniak M (2015) One-class classifiers with incremental learning and forgetting for data streams with concept drift. Soft Comput 19(12):3387–3400. https://doi.org/10.1007/s00500-014-1492-5

    Article  Google Scholar 

  17. LeCun Y, Cortes C (2019) MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist/. Cited on 2019-29-01

  18. LeCun YA, Bottou L, Orr GB, Müller KR (2012) Efficient backprop. In: Neural networks: Tricks of the trade. Springer, New York, pp 9–48

  19. Lesort T, Lomonaco V, Stoian A, Maltoni D, Filliat D, Díaz-Rodríguez N (2020) Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges. Inform Fusion 58:52–68. https://doi.org/10.1016/j.inffus.2019.12.004

    Article  Google Scholar 

  20. Marchi E, Vesperini F, Squartini S, Schuller B (2017) Deep recurrent neural network-based autoencoders for acoustic novelty detection. Comput Intell Neurosci 2017. https://doi.org/10.1155/2017/4694860

    Article  Google Scholar 

  21. Marsland S, Shapiro J, Nehmzow U (2002) A self-organising network that grows when required. Neural networks? Off J Int Neural Netw Soc 15(8–9):1041–1058. https://doi.org/10.1016/s0893-6080(02)00078-3

  22. McInnes L, Healy J, Saul N, Großberger L (2018) Umap: uniform manifold approximation and projection. J Open Sour Software 3(29):861

    Article  Google Scholar 

  23. Mustafa AM, Ayoade G, Al-Naami K, Khan L, Hamlen KW, Thuraisingham B, Araujo F (2017) Unsupervised deep embedding for novel class detection over data stream. In: IEEE international conference on Big Data, pp 1830–1839 . https://doi.org/10.1109/BigData.2017.8258127

  24. Nguyen TTT, Nguyen TT, Liew AWC, Wang SL (2018) Variational inference based Bayes online classifiers with concept drift adaptation. Pattern Recogn 81:280–293. https://doi.org/10.1016/j.patcog.2018.04.007

    Article  Google Scholar 

  25. Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier GANs. In: International conference on machine learning (ICML), pp 2642–2651

  26. Parisi GI, Tani J, Weber C, Wermter S (2018) Lifelong learning of spatiotemporal representations with dual-memory recurrent self-organization. Front Neurorobot 12:78. https://doi.org/10.3389/fnbot.2018.00078

    Article  Google Scholar 

  27. Rios A, Itti L(2019) Closed-loop memory gan for continual learning. In: International joint conference on artificial intelligence (IJCAI), pp 3332–3338 . https://doi.org/10.24963/ijcai.2019/462

  28. Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211–252. https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  29. Shin H, Lee JK, Kim J, Kim, J (2017) Continual learning with deep generative replay. In: Advances in neural information processing systems, pp 2990–2999

  30. Silver DL, Mercer RE (2002) The task rehearsal method of life-long learning: overcoming impoverished data. In: Cohen R, Spencer B (eds) Advances in artificial intelligence. Springer, Berlin, pp 90–101. https://doi.org/10.1007/3-540-47922-8_8

  31. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y (eds) 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, conference track proceedings. arxiv: 1409.1556

  32. Szadkowski R, Drchal J, Faigl J (2019) Basic evaluation scenarios for incrementally trained classifiers. In: International conference on artificial neural networks (ICANN), pp 507–517. https://doi.org/10.1007/978-3-030-30484-3_41

  33. Torralba A, Fergus R, Freeman WT (2008) 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans Pattern Anal Mach Intell 30(11):1958–1970. https://doi.org/10.1109/TPAMI.2008.128

    Article  Google Scholar 

Download references

Acknowledgements

This work is an extension of the paper presented at the 13th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM 2019), where it received the Best Student Paper award.

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27, Prague 6, Czech Republic

    Rudolf Szadkowski, Jan Drchal & Jan Faigl

Authors
  1. Rudolf Szadkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Drchal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Faigl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rudolf Szadkowski.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

This work was supported by the Czech Science Foundation (GAČR) under research project No. 18-18858S.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Szadkowski, R., Drchal, J. & Faigl, J. Continually trained life-long classification. Neural Comput & Applic 34, 135–152 (2022). https://doi.org/10.1007/s00521-021-06154-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06154-9

Keywords

Search

Navigation