Scalable and Order-robust Continual Learning with Additive Parameter Decomposition

Abstract

While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely varies based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameters for each task as a sum of task-shared and sparse task-adaptive parameters. With our Additive Parameter Decomposition (APD), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, we can achieve even better scalability with APD using hierarchical knowledge consolidation, which clusters the task-adaptive parameters to obtain hierarchically shared parameters. We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness.

Published in "International Conference on Learning Representation (ICLR)" 2020

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Cited by in corpus (8)

• Variational Federated Multi-Task Learning
• Meta-Consolidation for Continual Learning
• Continual Learning with Adaptive Weights (CLAW)
• Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
• Progressive Memory Banks for Incremental Domain Adaptation
• HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections
• Derivative-Free & Order-Robust Optimisation
• Group and Exclusive Sparse Regularization-based Continual Learning of CNNs