Modern deep-learning systems are specialized to problem settings in which training occurs once and then never again, as opposed to continual-learning settings in which training occurs continually. If deep-learning systems are applied in a continual learning setting, then it is well known that they may fail catastrophically to remember earlier examples. More fundamental, but less well known, is that they may also lose their ability to adapt to new data, a phenomenon called \textit{loss of plasticity}. We show loss of plasticity using the MNIST and ImageNet datasets repurposed for continual learning as sequences of tasks. In ImageNet, binary classification performance dropped from 89% correct on an early task down to 77%, or to about the level of a linear network, on the 2000th task. Such loss of plasticity occurred with a wide range of deep network architectures, optimizers, and activation functions, and was not eased by batch normalization or dropout. In our experiments, loss of plasticity was correlated with the proliferation of dead units, with very large weights, and more generally with a loss of unit diversity. Loss of plasticity was substantially eased by $L^2$-regularization, particularly when combined with weight perturbation (Shrink and Perturb). We show that plasticity can be fully maintained by a new algorithm -- called $\textit{continual backpropagation}$ -- which is just like conventional backpropagation except that a small fraction of less-used units are reinitialized after each example.

本文研究了深度学习系统在持续学习环境下的表现，发现其容易出现失去可塑性现象，影响其对新数据的适应能力，但通过L2正则化和重启动某些不常用单元的连续反向传播算法，可以缓解和避免这种现象。

深度持续学习中的可塑性维护