Understand
Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills.
- The lack of consensus in evaluating continual learning algorithms and the almost exclusive focus on forgetting motivate us to propose a more comprehensive set of implementation independent metrics accounting for several factors we believe have practical implications worth considering in the deployment of real AI systems that learn continually: accuracy or performance over time, backward and forward knowledge transfer, memory overhead as well as computational efficiency.
- Drawing inspiration from the standard Multi-Attribute Value Theory (MAVT) we further propose to fuse these metrics into a single score for ranking purposes and we evaluate our proposal with five continual learning strategies on the iCIFAR-100 continual learning benchmark.
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Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J. (1989) · 1989
Earlier work this paper cites.
Decision with multiple objectives, preferences and value tradeoffs
Keeney, R. and Raiffa, H. (1993) · 1993
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
French, R. M. (1999) · 1999
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2009) · 2009
Earlier work this paper cites.
Multi-criteria decision analysis: methods and software
Ishizaka, A. and Nemery, P. (2013) · 2013
Earlier work this paper cites.
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