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: https://github.com/Ivsucram/CDCL

本研究提出了一种名为交叉领域连续学习（CDCL）的新方法，在一个紧凑的卷积网络中结合了跨任务关注机制，实现了对先前任务特征的对齐，并在相关领域之间进行了无监督的交叉领域学习（UDA）。通过使用一种任务内特定的伪标签方法，确保了有标签和无标签样本的准确输入对，从而增强了学习过程。本方法在公开的 UDA 数据集上进行了广泛实验，显示出在交叉领域连续学习挑战上的良好性能。此外，本研究提出了增量思想，为该领域的进展做出了贡献。

跨领域连续学习

Towards Cross-Domain Continual Learning

Gait, i.e., the movement pattern of human limbs during locomotion, is a
promising biometric for the identification of persons. Despite significant
improvement in gait recognition with deep learning, existing studies still
neglect a more practical but challenging scenario -- unsupervised cross-domain
gait recognition which aims to learn a model on a labeled dataset then adapts
it to an unlabeled dataset. Due to the domain shift and class gap, directly
applying a model trained on one source dataset to other target datasets usually
obtains very poor results. Therefore, this paper proposes a Transferable
Neighborhood Discovery (TraND) framework to bridge the domain gap for
unsupervised cross-domain gait recognition. To learn effective prior knowledge
for gait representation, we first adopt a backbone network pre-trained on the
labeled source data in a supervised manner. Then we design an end-to-end
trainable approach to automatically discover the confident neighborhoods of
unlabeled samples in the latent space. During training, the class consistency
indicator is adopted to select confident neighborhoods of samples based on
their entropy measurements. Moreover, we explore a high-entropy-first neighbor
selection strategy, which can effectively transfer prior knowledge to the
target domain. Our method achieves state-of-the-art results on two public
datasets, i.e., CASIA-B and OU-LP.

本文提出了一种名为 TraND 的跨域步态识别框架，通过深度学习和无监督学习，使用经过训练的模型来自动地发现未标记样本的自信邻域，然后使用高熵优先邻域选择策略来转移先前学习到的信息，从而对 CASIA-B 和 OU-LP 等两个公共数据集进行跨域步态识别，并取得了最新的最佳结果。