Directions of Curvature as an Explanation for Loss of Plasticity
This addresses a fundamental issue in continual learning for AI systems, though it is incremental as it builds on prior observations without introducing a new paradigm.
The paper tackles the problem of loss of plasticity in neural networks by attributing it to a reduction in curvature directions during training, and shows that regularizers preserving curvature mitigate this issue across MNIST, CIFAR-10, and ImageNet tasks.
Loss of plasticity is a phenomenon in which neural networks lose their ability to learn from new experience. Despite being empirically observed in several problem settings, little is understood about the mechanisms that lead to loss of plasticity. In this paper, we offer a consistent explanation for loss of plasticity: Neural networks lose directions of curvature during training and that loss of plasticity can be attributed to this reduction in curvature. To support such a claim, we provide a systematic investigation of loss of plasticity across continual learning tasks using MNIST, CIFAR-10 and ImageNet. Our findings illustrate that loss of curvature directions coincides with loss of plasticity, while also showing that previous explanations are insufficient to explain loss of plasticity in all settings. Lastly, we show that regularizers which mitigate loss of plasticity also preserve curvature, motivating a simple distributional regularizer that proves to be effective across the problem settings we considered.