We introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at lifelong learning in fixed capacity models based on neuronal model sparsification. In this method, subsequent tasks are trained using the inactive neurons and filters of the sparsified network and cause zero deterioration to the performance of previous tasks. In order to deal with the possible compromise between model sparsity and performance, we formalize and incorporate the concept of graceful forgetting: the idea that it is preferable to suffer a small amount of forgetting in a controlled manner if it helps regain network capacity and prevents uncontrolled loss of performance during the training of future tasks. CLNP also provides simple continual learning diagnostic tools in terms of the number of free neurons left for the training of future tasks as well as the number of neurons that are being reused. In particular, we see in experiments that CLNP verifies and automatically takes advantage of the fact that the features of earlier layers are more transferable. We show empirically that CLNP leads to significantly improved results over current weight elasticity based methods.

本篇研究提出了一种名为CLNP的方法，通过神经模型稀疏化实现模型固定容量下的全生命周期学习，在这种方法中，使用经过稀疏化网络中的非活动神经元和滤波器来训练后续任务，并对以前任务的性能不会造成任何恶化，此外，CLNP还提供了简单的学习诊断工具。实验证明，与当前基于权重弹性的方法相比，CLNP能够显著提高结果。

神经修剪的连续学习