Addressing Catastrophic Forgetting in Few-Shot Problems
This addresses a critical issue for few-shot learning systems that process sequential tasks, though it is incremental as it builds on existing meta-learning methods.
The paper tackled catastrophic forgetting in few-shot classification by introducing a Bayesian online meta-learning framework, which effectively reduced forgetting compared to baselines.
Neural networks are known to suffer from catastrophic forgetting when trained on sequential datasets. While there have been numerous attempts to solve this problem in large-scale supervised classification, little has been done to overcome catastrophic forgetting in few-shot classification problems. We demonstrate that the popular gradient-based model-agnostic meta-learning algorithm (MAML) indeed suffers from catastrophic forgetting and introduce a Bayesian online meta-learning framework that tackles this problem. Our framework utilises Bayesian online learning and meta-learning along with Laplace approximation and variational inference to overcome catastrophic forgetting in few-shot classification problems. The experimental evaluations demonstrate that our framework can effectively achieve this goal in comparison with various baselines. As an additional utility, we also demonstrate empirically that our framework is capable of meta-learning on sequentially arriving few-shot tasks from a stationary task distribution.