Continual learning aims to empower artificial intelligence (AI) with strong
adaptability to the real world. For this purpose, a desirable solution should
properly balance memory stability with learning plasticity, and acquire
sufficient compatibility to capture the observed distributions. Existing
advances mainly focus on preserving memory stability to overcome catastrophic
forgetting, but remain difficult to flexibly accommodate incremental changes as
biological intelligence (BI) does. By modeling a robust Drosophila learning
system that actively regulates forgetting with multiple learning modules, here
we propose a generic approach that appropriately attenuates old memories in
parameter distributions to improve learning plasticity, and accordingly
coordinates a multi-learner architecture to ensure solution compatibility.
Through extensive theoretical and empirical validation, our approach not only
clearly enhances the performance of continual learning, especially over
synaptic regularization methods in task-incremental settings, but also
potentially advances the understanding of neurological adaptive mechanisms,
serving as a novel paradigm to progress AI and BI together.

通过建立一个稳定性与可塑性均衡、具有多个学习模块的学习系统，我们提出了一种通用方法，可以适度减弱旧记忆在参数分布中的影响，以提高学习可塑性，并通过协调多学习者架构确保解决方案的兼容性。经过大量的理论和实证验证，我们的方法不仅明显提高了持续学习的性能，尤其是在任务增量设置中超过了突触正则化方法，还可能推进神经适应机制的理解，成为推动人工智能和生物智能共同进步的新范例。

融合神经启发的适应性在人工智能中持续学习

Incorporating Neuro-Inspired Adaptability for Continual Learning in  Artificial Intelligence

Continual learning requires incremental compatibility with a sequence of
tasks. However, the design of model architecture remains an open question: In
general, learning all tasks with a shared set of parameters suffers from severe
interference between tasks; while learning each task with a dedicated parameter
subspace is limited by scalability. In this work, we theoretically analyze the
generalization errors for learning plasticity and memory stability in continual
learning, which can be uniformly upper-bounded by (1) discrepancy between task
distributions, (2) flatness of loss landscape and (3) cover of parameter space.
Then, inspired by the robust biological learning system that processes
sequential experiences with multiple parallel compartments, we propose
Cooperation of Small Continual Learners (CoSCL) as a general strategy for
continual learning. Specifically, we present an architecture with a fixed
number of narrower sub-networks to learn all incremental tasks in parallel,
which can naturally reduce the two errors through improving the three
components of the upper bound. To strengthen this advantage, we encourage to
cooperate these sub-networks by penalizing the difference of predictions made
by their feature representations. With a fixed parameter budget, CoSCL can
improve a variety of representative continual learning approaches by a large
margin (e.g., up to 10.64% on CIFAR-100-SC, 9.33% on CIFAR-100-RS, 11.45% on
CUB-200-2011 and 6.72% on Tiny-ImageNet) and achieve the new state-of-the-art
performance.

本文研究了在环境改变时如何使模型具有可持续发展能力，在理论层面上证明了增量学习的可行性，并提出了一种新的模型架构 ——Cooperation of Small Continual Learners (CoSCL)，该模型使用一组独立的子网络来学习所有的任务，有助于提高模型的泛化能力和记忆稳定性，并取得了新的最优性能。