基于稳定表征与经验回放的连续无监督域适应
Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay
AI总结：
针对持续学习场景下无监督域适应需同时访问源目标域数据、易发生灾难性遗忘的问题，提出基于GMM稳定内部分布+经验回放的算法，经理论分析与4个基准实验验证有效。
摘要：
我们提出了一种用于解决持续学习（CL）场景下无监督域适应（UDA）问题的算法。其核心目标是：当新域持续到达、且后续任务仅能获取无标签数据时，通过更新基础模型，维持模型在域偏移下的泛化能力。现有UDA算法虽数量众多，但通常要求同时获取源域和目标域数据集；而现有CL方法仅能处理全部带有标签数据的任务。我们的方案以稳定已学习的内部分布为基础，以此提升模型在新域上的泛化能力，该内部分布通过隐藏层的网络响应进行建模。我们采用高斯混合模型（Gaussian mixture model, GMM）对这一内部分布建模，并通过将新域的内部学习分布与估计得到的GMM进行匹配来更新模型。此外，我们利用经验回放机制来解决灾难性遗忘问题——即模型在学习新任务时会丢失先前习得的知识。我们提供了理论分析以解释算法的有效性原理，还开展了大量对比与分析实验来验证方法的效果，并在4个基准数据集上进行了实验，证明了所提方法的有效性。
英文摘要：
We introduce an algorithm for tackling the problem of unsupervised domain adaptation (UDA) in continual learning (CL) scenarios. The primary objective is to maintain model generalization under domain shift when new domains arrive continually through updating a base model when only unlabeled data is accessible in subsequent tasks. While there are many existing UDA algorithms, they typically require access to both the source and target domain datasets simultaneously. Conversely, existing CL approaches can handle tasks that all have labeled data. Our solution is based on stabilizing the learned internal distribution to enhances the model generalization on new domains. The internal distribution is modeled by network responses in hidden layer. We model this internal distribution using a Gaussian mixture model (GMM ) and update the model by matching the internally learned distribution of new domains to the estimated GMM. Additionally, we leverage experience replay to overcome the problem of catastrophic forgetting, where the model loses previously acquired knowledge when learning new tasks. We offer theoretical analysis to explain why our algorithm would work. We also offer extensive comparative and analytic experiments to demonstrate that our method is effective. We perform experiments on four benchmark datasets to demonstrate that our approach is effective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
