Towards Cross-Domain Continual Learning
Marcus de Carvalho Mahardhika Pratama Jie Zhang Chua Haoyan Edward Yapp
Continual learning is a process that involves training learning agents to
sequentially master a stream of tasks or classes without revisiting past data.
The challenge lies in leveraging previously acquired knowledge to learn new
tasks efficiently, while avoiding catastrophic forgetting. Existing methods
primarily focus on single domains, restricting their applicability to specific
problems.
  In this work, we introduce a novel approach called Cross-Domain Continual
Learning (CDCL) that addresses the limitations of being limited to single
supervised domains. Our method combines inter- and intra-task cross-attention
mechanisms within a compact convolutional network. This integration enables the
model to maintain alignment with features from previous tasks, thereby delaying
the data drift that may occur between tasks, while performing unsupervised
cross-domain (UDA) between related domains. By leveraging an
intra-task-specific pseudo-labeling method, we ensure accurate input pairs for
both labeled and unlabeled samples, enhancing the learning process. To validate
our approach, we conduct extensive experiments on public UDA datasets,
showcasing its positive performance on cross-domain continual learning
challenges. Additionally, our work introduces incremental ideas that contribute
to the advancement of this field.
  We make our code and models available to encourage further exploration and
reproduction of our results: \url{https://github.com/Ivsucram/CDCL}