Reducing Catastrophic Forgetting in Modular Neural Networks by Dynamic Information Balancing
This addresses the problem of catastrophic forgetting for lifelong learning systems, representing an incremental improvement by combining modular routing with existing regularization techniques.
The paper tackles catastrophic forgetting in neural networks during continual learning by introducing dynamic information balancing (DIB), which routes inputs to modules based on information load to minimize interference, and shows that DIB combined with elastic weight consolidation (EWC) outperforms similar models with EWC alone across various tasks and datasets.
Lifelong learning is a very important step toward realizing robust autonomous artificial agents. Neural networks are the main engine of deep learning, which is the current state-of-the-art technique in formulating adaptive artificial intelligent systems. However, neural networks suffer from catastrophic forgetting when stressed with the challenge of continual learning. We investigate how to exploit modular topology in neural networks in order to dynamically balance the information load between different modules by routing inputs based on the information content in each module so that information interference is minimized. Our dynamic information balancing (DIB) technique adapts a reinforcement learning technique to guide the routing of different inputs based on a reward signal derived from a measure of the information load in each module. Our empirical results show that DIB combined with elastic weight consolidation (EWC) regularization outperforms models with similar capacity and EWC regularization across different task formulations and datasets.