Catastrophic Forgetting (CF) is a prominent issue in continual learning.
Parameter isolation addresses this challenge by masking a sub-network for each
task to mitigate interference with old tasks. However, these sub-networks are
constructed relying on weight magnitude, which does not necessarily correspond
to the importance of weights, resulting in maintaining unimportant weights and
constructing redundant sub-networks. To overcome this limitation, inspired by
information bottleneck, which removes redundancy between adjacent network
layers, we propose \textbf{\underline{I}nformation \underline{B}ottleneck
\underline{M}asked sub-network (IBM)} to eliminate redundancy within
sub-networks. Specifically, IBM accumulates valuable information into essential
weights to construct redundancy-free sub-networks, not only effectively
mitigating CF by freezing the sub-networks but also facilitating new tasks
training through the transfer of valuable knowledge. Additionally, IBM
decomposes hidden representations to automate the construction process and make
it flexible. Extensive experiments demonstrate that IBM consistently
outperforms state-of-the-art methods. Notably, IBM surpasses the
state-of-the-art parameter isolation method with a 70\% reduction in the number
of parameters within sub-networks and an 80\% decrease in training time.

为了克服参数隔离方法中存在的限制，我们提出了信息瓶颈掩码子网（IBM）方法，通过消除子网络中的冗余，累积宝贵的信息到关键权重，构建不带冗余的子网络，有效地减轻了灾难性遗忘问题，并通过传输宝贵的知识促进新任务训练。实验证明，IBM 方法一直优于现有的方法，尤其是与参数隔离方法相比，IBM 方法在子网络参数数量上减少了 70％，同时训练时间减少了 80％。