Uncertainty-based Continual Learning with Adaptive Regularization
Hongjoon Ahn · Sungmin Cha · Donggyu Lee · Taesup Moon

Wed Dec 11th 05:00 -- 07:00 PM @ East Exhibition Hall B + C #47

We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learning framework with variational inference. We focus on two significant drawbacks of the recently proposed regularization-based methods: a) considerable additional memory cost for determining the per-weight regularization strengths and b) the absence of gracefully forgetting scheme, which can prevent performance degradation in learning new tasks. In this paper, we show UCL can solve these two problems by introducing a fresh interpretation on the Kullback-Leibler (KL) divergence term of the variational lower bound for Gaussian mean-field approximation. Based on the interpretation, we propose the notion of node-wise uncertainty, which drastically reduces the number of additional parameters for implementing per-weight regularization. Moreover, we devise two additional regularization terms that enforce \emph{stability} by freezing important parameters for past tasks and allow \emph{plasticity} by controlling the actively learning parameters for a new task. Through extensive experiments, we show UCL convincingly outperforms most of recent state-of-the-art baselines not only on popular supervised learning benchmarks, but also on challenging lifelong reinforcement learning tasks. The source code of our algorithm is available at

Author Information

Hongjoon Ahn (Sunkyunkwan University)
Sungmin Cha (Sungkyunkwan University)
Donggyu Lee (Sungkyunkwan university)
Taesup Moon (Sungkyunkwan University (SKKU))

Taesup Moon is currently an associate professor at Sungkyunkwan University (SKKU), Korea. Prior to joining SKKU in 2017, he was an assistant professor at DGIST from 2015 to 2017, a research staff member at Samsung Advanced Institute of Technology (SAIT) from 2013 to 2015, a postdoctoral researcher at UC Berkeley, Statistics from 2012 to 2013, and a research scientist at Yahoo! Labs from 2008 to 2012. He got his Ph.D. and MS degrees in Electrical Engineering from Stanford University, CA USA in 2008 and 2004, respectively, and his BS degree in Electrical Engineering from Seoul National University, Korea in 2002. His research interests are in deep learning, statistical machine learning, data science, signal processing, and information theory.

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