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End-to-End Incremental Learning

  • Francisco M. CastroEmail author
  • Manuel J. Marín-Jiménez
  • Nicolás Guil
  • Cordelia Schmid
  • Karteek Alahari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11216)

Abstract

Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model—a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees. We evaluate our method extensively on the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and show state-of-the-art performance.

Keywords

Incremental learning CNN Distillation loss Image classification 

Notes

Acknowledgements

This work was supported in part by the projects TIC-1692 (Junta de Andalucía), TIN2016-80920R (Spanish Ministry of Science and Tech.), ERC advanced grant ALLEGRO, and EVEREST (no. 5302-1) funded by CEFIPRA. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal GPU used for this research.

Supplementary material

474200_1_En_15_MOESM1_ESM.pdf (302 kb)
Supplementary material 1 (pdf 302 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Francisco M. Castro
    • 1
    Email author
  • Manuel J. Marín-Jiménez
    • 2
  • Nicolás Guil
    • 1
  • Cordelia Schmid
    • 3
  • Karteek Alahari
    • 3
  1. 1.Department of Computer ArchitectureUniversity of MálagaMálagaSpain
  2. 2.Department of Computing and Numerical AnalysisUniversity of CórdobaCórdobaSpain
  3. 3.Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJKGrenobleFrance

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