  This paper presents a data-free, parameter-isolation-based continual learning
algorithm we developed for the sequential task continual learning track of the
2nd Greater Bay Area International Algorithm Competition. The method learns an
independent parameter subspace for each task within the network's convolutional
and linear layers and freezes the batch normalization layers after the first
task. Specifically, for domain incremental setting where all domains share a
classification head, we freeze the shared classification head after first task
is completed, effectively solving the issue of catastrophic forgetting.
Additionally, facing the challenge of domain incremental settings without
providing a task identity, we designed an inference task identity strategy,
selecting an appropriate mask matrix for each sample. Furthermore, we
introduced a gradient supplementation strategy to enhance the importance of
unselected parameters for the current task, facilitating learning for new
tasks. We also implemented an adaptive importance scoring strategy that
dynamically adjusts the amount of parameters to optimize single-task
performance while reducing parameter usage. Moreover, considering the
limitations of storage space and inference time, we designed a mask matrix
compression strategy to save storage space and improve the speed of encryption
and decryption of the mask matrix. Our approach does not require expanding the
core network or using external auxiliary networks or data, and performs well
under both task incremental and domain incremental settings. This solution
ultimately won a second-place prize in the competition.
