The ability to continuously learn remains elusive for deep learning models.
Unlike humans, models cannot accumulate knowledge in their weights when
learning new tasks, mainly due to an excess of plasticity and the low incentive
to reuse weights when training a new task. To address the stability-plasticity
dilemma in neural networks, we propose a novel method called Entropy-based
Stability-Plasticity (ESP). Our approach can decide dynamically how much each
model layer should be modified via a plasticity factor. We incorporate branch
layers and an entropy-based criterion into the model to find such factor. Our
experiments in the domains of natural language and vision show the
effectiveness of our approach in leveraging prior knowledge by reducing
interference. Also, in some cases, it is possible to freeze layers during
training leading to speed up in training.

提出了一种基于熵的稳定性可塑性方法来解决神经网络中的稳定性 - 可塑性困境，该方法在自然语言和图像领域的实验表明其可以通过减少干扰来利用先前知识，并在某些情况下可以冻结层以加速训练。