  Managing heterogeneous datasets that vary in complexity, size, and similarity
in continual learning presents a significant challenge. Task-agnostic continual
learning is necessary to address this challenge, as datasets with varying
similarity pose difficulties in distinguishing task boundaries. Conventional
task-agnostic continual learning practices typically rely on rehearsal or
regularization techniques. However, rehearsal methods may struggle with varying
dataset sizes and regulating the importance of old and new data due to rigid
buffer sizes. Meanwhile, regularization methods apply generic constraints to
promote generalization but can hinder performance when dealing with dissimilar
datasets lacking shared features, necessitating a more adaptive approach. In
this paper, we propose AdaptCL, a novel adaptive continual learning method to
tackle heterogeneity in sequential datasets. AdaptCL employs fine-grained
data-driven pruning to adapt to variations in data complexity and dataset size.
It also utilizes task-agnostic parameter isolation to mitigate the impact of
varying degrees of catastrophic forgetting caused by differences in data
similarity. Through a two-pronged case study approach, we evaluate AdaptCL on
both datasets of MNIST Variants and DomainNet, as well as datasets from
different domains. The latter include both large-scale, diverse binary-class
datasets and few-shot, multi-class datasets. Across all these scenarios,
AdaptCL consistently exhibits robust performance, demonstrating its flexibility
and general applicability in handling heterogeneous datasets.
