Uncertainty-based Continual Learning with Adaptive Regularization (1905.11614v3)

Abstract: 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 stability by freezing important parameters for past tasks and allow 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 https://github.com/csm9493/UCL.

• The paper introduces UCL, a novel method that reinterprets the KL divergence term to capture node-wise uncertainty.
• It employs two adaptive regularization terms to balance stability and flexibility in learning new tasks without high memory costs.
• Extensive experiments show UCL’s superior performance over benchmarks, offering a practical solution for continual learning challenges.

Uncertainty-based Continual Learning with Adaptive Regularization

The paper "Uncertainty-based Continual Learning with Adaptive Regularization," explores a novel approach to continual learning in neural networks. Named Uncertainty-regularized Continual Learning (UCL), this approach builds on the traditional Bayesian online learning framework interfacing through variational inference. The authors highlight two major drawbacks of prevailing regularization-based methods in continual learning: the significant memory cost for per-weight regularization and the lack of mechanisms for graceful forgetting.

A key innovation in this work is the reinterpretation of the Kullback-Leibler (KL) divergence term in the variational lower bound for Gaussian mean-field approximation. This reinterpretation leads to the introduction of a node-wise uncertainty measure, which substantially reduces the number of additional parameters needed compared to existing state-of-the-art methods, while also aiming to stabilize previous task learning and adapt to new tasks through structured regularization.

Methodology and Contributions

1. Bayesian Framework Reinterpretation: The authors reformulate the KL divergence term in the variational lower bound, introducing a node-wise uncertainty concept that captures the importance of network nodes rather than individual weights.
2. Regularization Methods: Two additional regularization terms are introduced to the learning process. These terms are designed to enforce stability by freezing parameters critical for previous tasks and to facilitate learning flexibility by controlling parameter updates for new tasks.
3. Extensive Experimental Validation: UCL showcases superior performance across multiple supervised learning benchmarks, including Permuated MNIST, Row-permuted MNIST, Split MNIST, Split notMNIST, CIFAR datasets, and Omniglot datasets, as well as on challenging lifelong reinforcement learning tasks. UCL is noted not only for its reduced parameter count—being more memory-efficient than VCL—but also for achieving higher task performance across learning contexts.

Implications and Future Prospects

The development of UCL represents an important step forward in addressing the stability-plasticity dilemma inherent in continual learning tasks. The ability to reduce parametric overhead while maintaining or improving learning performance is crucial for deploying neural networks on resource-constrained systems.

The results suggest practical implications for application in both supervised and reinforcement learning settings, where adaptability and memory efficiency are of paramount importance. The theoretical perspective provided by considering node-wise uncertainty rather than individual weight uncertainty may lead to new research avenues in network design and understanding neural representation at a higher abstraction level.

Moving forward, extending UCL to more complex network architectures and tasks, and exploring potential integrations with other learning paradigms (e.g., meta-learning or transfer learning), could leverage its benefits further. The field should also examine the potential for combining UCL's framework with dynamic architectures that adjust network topology in response to task needs, further enhancing resource efficiency and task adaptability.

In conclusion, UCL presents a refined and efficient approach to continual learning by balancing the tension between parameter efficiency and learning performance, addressing critical challenges in the deployment of scalable, adaptive neural network models.