Incremental Learning Through Deep Adaptation

@article{Rosenfeld2017IncrementalLT,
  title={Incremental Learning Through Deep Adaptation},
  author={Amir Rosenfeld and John K. Tsotsos},
  journal={CoRR},
  year={2017},
  volume={abs/1705.04228}
}
Given an existing trained neural network, it is often desirable to be able to add new capabilities without hindering performance of already learned tasks. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added task, typically as many as the original network. We propose a method which fully preserves performance on the original task, with only a small increase (around 20%) in the number of… CONTINUE READING
6 Citations
28 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 28 references

Caltech-256 object category dataset

  • Gregory Griffin, Alex Holub, Pietro Perona
  • 2007
Highly Influential
7 Excerpts

Similar Papers

Loading similar papers…