Less-Forgetful Learning for Domain Expansion in Deep Neural Networks

@inproceedings{Jung2018LessForgetfulLF,
  title={Less-Forgetful Learning for Domain Expansion in Deep Neural Networks},
  author={Heechul Jung and Jeongwoo Ju and Minju Jung and Junmo Kim},
  booktitle={AAAI},
  year={2018}
}
Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain. In this paper, we propose a less-forgetful learning method for the domain expansion scenario. While existing domain adaptation techniques solely focused on adapting to new domains, the proposed technique focuses on working well with both old and new domains without… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 24 REFERENCES

An empirical investigation of catastrophic forgeting in gradient-based neural networks

  • I. J. Goodfellow, M. Mirza, D. Xiao, A. Courville, Y. Bengio
  • arXiv preprint arXiv:1312.6211.
  • 2013
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