# Reducing Catastrophic Forgetting in Modular Neural Networks by Dynamic Information Balancing

arXiv: https://arxiv.org/abs/1912.04508 · HTML: https://mlsanity.com/paper/1912.04508/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation](https://mlsanity.com/paper/2209.06107/) (2022)
- [Detecting and Learning the Unknown in Semantic Segmentation](https://mlsanity.com/paper/2202.08700/) (2022)
- [A Stepwise Distillation Learning Strategy for Non-differentiable Visual Programming Frameworks on Visual Reasoning Tasks](https://mlsanity.com/paper/2309.09809/) (2023)
- [How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence](https://mlsanity.com/paper/2603.18203/) (2026)

## Abstract (arXiv)

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.