Continual evaluation for lifelong learning: Identifying the stability gapDownload PDF

Published: 01 Feb 2023, Last Modified: 01 Mar 2023ICLR 2023 notable top 25%Readers: Everyone
Keywords: Continual learning, lifelong learning, incremental learning, evaluation metrics
TL;DR: Proposing an iteration-based continual evaluation framework for CL, we discover, quantify, and analyse the "stability gap", a phenomenon where upon learning new tasks, past tasks exhibit substantial but transient performance loss for SOTA CL methods.
Abstract: Time-dependent data-generating distributions have proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previously learned knowledge. Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of common state-of-the-art methods still suffers from substantial forgetting upon starting to learn new tasks, except that this forgetting is temporary and followed by a phase of performance recovery. We refer to this intriguing but potentially problematic phenomenon as the stability gap. The stability gap had likely remained under the radar due to standard practice in the field of evaluating continual learning models only after each task. Instead, we establish a framework for continual evaluation that uses per-iteration evaluation and we define a new set of metrics to quantify worst-case performance. Empirically we show that experience replay, constraint-based replay, knowledge-distillation, and parameter regularization methods are all prone to the stability gap; and that the stability gap can be observed in class-, task-, and domain-incremental learning benchmarks. Additionally, a controlled experiment shows that the stability gap increases when tasks are more dissimilar. Finally, by disentangling gradients into plasticity and stability components, we propose a conceptual explanation for the stability gap.
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