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Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer
Seungwon Lee · Sima Behpour · Eric Eaton

Thu Jul 22 05:35 AM -- 05:40 AM (PDT) @
in Reinforcement Learning 15 »

Effective lifelong learning across diverse tasks requires the transfer of diverse knowledge, yet transferring irrelevant knowledge may lead to interference and catastrophic forgetting. In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks. We first show that the lifelong learning performance of several current deep learning architectures can be significantly improved by transfer at the appropriate layers. We then develop an expectation-maximization (EM) method to automatically select the appropriate transfer configuration and optimize the task network weights. This EM-based selective transfer is highly effective, balancing transfer performance on all tasks with avoiding catastrophic forgetting, as demonstrated on three algorithms in several lifelong object classification scenarios.

Keywords: Algorithms · Multitask, Transfer, and Meta Learning

Author Information

Seungwon Lee (University of Pennsylvania)
Sima Behpour (Carnegie Mellon University)
Eric Eaton (University of Pennsylvania)

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