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arXiv 2109.14035cs.LGcs.AImath.DSmath.OC

Formalizing the Generalization-Forgetting Trade-off in Continual Learning

Argonne National Laboratory(阿贡国家实验室)

Krishnan Raghavan, Prasanna Balaprakash

英文摘要:

We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two-player sequential game. In this approach, player 1 maximizes the cost due to lack of generalization whereas player 2 minimizes the cost due to catastrophic forgetting. We show theoretically that a balance point between the two players exists for each task and that this point is stable (once the balance is achieved, the two players stay at the balance point). Next, we introduce balanced continual learning (BCL), which is designed to attain balance between generalization and forgetting and empirically demonstrate that BCL is comparable to or better than the state of the art.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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