We introduce a novel continual learning method based on multifidelity deep
neural networks. This method learns the correlation between the output of
previously trained models and the desired output of the model on the current
training dataset, limiting catastrophic forgetting. On its own the
multifidelity continual learning method shows robust results that limit
forgetting across several datasets. Additionally, we show that the
multifidelity method can be combined with existing continual learning methods,
including replay and memory aware synapses, to further limit catastrophic
forgetting. The proposed continual learning method is especially suited for
physical problems where the data satisfy the same physical laws on each domain,
or for physics-informed neural networks, because in these cases we expect there
to be a strong correlation between the output of the previous model and the
model on the current training domain.

提出一种基于多保真深度神经网络的连续学习方法，限制灾难性遗忘，并能与已有的连续学习方法（包括重放和记忆感知突触）结合使用。该方法特别适合解决物理问题和基于物理的神经网络。

物理系统连续学习的多保真度方法

A multifidelity approach to continual learning for physical systems

Methods proposed in the literature towards continual deep learning typically
operate in a task-based sequential learning setup. A sequence of tasks is
learned, one at a time, with all data of current task available but not of
previous or future tasks. Task boundaries and identities are known at all
times. This setup, however, is rarely encountered in practical applications.
Therefore we investigate how to transform continual learning to an online
setup. We develop a system that keeps on learning over time in a streaming
fashion, with data distributions gradually changing and without the notion of
separate tasks. To this end, we build on the work on Memory Aware Synapses, and
show how this method can be made online by providing a protocol to decide i)
when to update the importance weights, ii) which data to use to update them,
and iii) how to accumulate the importance weights at each update step.
Experimental results show the validity of the approach in the context of two
applications: (self-)supervised learning of a face recognition model by
watching soap series and learning a robot to avoid collisions.

探究如何将连续学习转变成在线学习的系统，可用于自我监督学习和机器人避免冲突，该系统采用 “内存感知神经元” 的方法更新关键性权重。

无任务持续学习

Task-Free Continual Learning

Humans can learn in a continuous manner. Old rarely utilized knowledge can be
overwritten by new incoming information while important, frequently used
knowledge is prevented from being erased. In artificial learning systems,
lifelong learning so far has focused mainly on accumulating knowledge over
tasks and overcoming catastrophic forgetting. In this paper, we argue that,
given the limited model capacity and the unlimited new information to be
learned, knowledge has to be preserved or erased selectively. Inspired by
neuroplasticity, we propose a novel approach for lifelong learning, coined
Memory Aware Synapses (MAS). It computes the importance of the parameters of a
neural network in an unsupervised and online manner. Given a new sample which
is fed to the network, MAS accumulates an importance measure for each parameter
of the network, based on how sensitive the predicted output function is to a
change in this parameter. When learning a new task, changes to important
parameters can then be penalized, effectively preventing important knowledge
related to previous tasks from being overwritten. Further, we show an
interesting connection between a local version of our method and Hebb's
rule,which is a model for the learning process in the brain. We test our method
on a sequence of object recognition tasks and on the challenging problem of
learning an embedding for predicting $<$subject, predicate, object$>$ triplets.
We show state-of-the-art performance and, for the first time, the ability to
adapt the importance of the parameters based on unlabeled data towards what the
network needs (not) to forget, which may vary depending on test conditions.

本文提出了一个新的生命周期学习的方法，命名为记忆感知突触 (Memory Aware Synapses)，它计算神经网络参数的重要性，并根据重要性进行惩罚以防止重要知识被覆盖，这与大脑学习过程的 Hebb 规则有相关性。在物体识别任务和嵌入学习任务中，该方法表现出最先进的性能。