CONTINUAL LEARNING IN RECURRENT NEURAL NETWORKS

Abstract

While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing sequential data with recurrent neural networks (RNNs) is lacking. Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks. Specifically, we shed light on the particularities that arise when applying weight-importance methods, such as elastic weight consolidation, to RNNs. In contrast to feedforward networks, RNNs iteratively reuse a shared set of weights and require working memory to process input samples. We show that the performance of weight-importance methods is not directly affected by the length of the processed sequences, but rather by high working memory requirements, which lead to an increased need for stability at the cost of decreased plasticity for learning subsequent tasks. We additionally provide theoretical arguments supporting this interpretation by studying linear RNNs. Our study shows that established CL methods can be successfully ported to the recurrent case, and that a recent regularization approach based on hypernetworks outperforms weight-importance methods, thus emerging as a promising candidate for CL in RNNs. Overall, we provide insights on the differences between CL in feedforward networks and RNNs, while guiding towards effective solutions to tackle CL on sequential data.

1. INTRODUCTION

The ability to continually learn from a non-stationary data distribution while transferring and protecting past knowledge is known as continual learning (CL). This ability requires neural networks to be stable to prevent forgetting, but also plastic to learn novel information, which is referred to as the stability-plasticity dilemma (Grossberg, 2007; Mermillod et al., 2013) . To address this dilemma, a variety of methods which tackle CL for static data with feedforward networks have been proposed (for reviews refer to Parisi et al. ( 2019) and van de Ven and Tolias ( 2019)). However, CL for sequential data has only received little attention, despite recent work confirming that recurrent neural networks (RNNs) also suffer from catastrophic forgetting (Schak and Gepperth, 2019) . A set of methods that holds great promise to address this problem are regularization methods, which work by constraining the update of certain parameters. These methods can be considered more versatile than competing approaches, since they do not require rehearsal of past data, nor an increase in model capacity, but can benefit from either of the two (e.g., Nguyen et al., 2018; Yoon et al., 2018) . This makes regularization methods applicable to a broader variety of situations, e.g. when issues related to data privacy, storage, or limited computational resources during inference might arise. The most well-known regularization methods are weight-importance methods, such as elastic weight consolidation (EWC, Kirkpatrick et al. (2017a) ) and synaptic intelligence (SI, Zenke et al. ( 2017)), which are based on assigning importance values to weights. Some of these have a direct probabilistic interpretation as prior-focused CL methods (Farquhar and Gal, 2018) , for which

