标题： 带有变分领域无关特征重放的连续域适应 显示英文标题

标题： Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay

摘要： 在非平稳环境中学习是机器学习中最大的挑战之一。 非平稳性可能由任务漂移引起，即给定输入数据的标签条件分布的变化，或者由领域漂移引起，即输入数据边缘分布的变化。 本文旨在解决连续领域自适应中的这一挑战，在这种情况下，模型需要在非平稳环境中学习适应新领域的新的任务，同时保持之前学到的知识。 为了应对这两种漂移，我们提出了变分领域无关特征重放，这是一种由三个组件组成的的方法：一个推理模块，用于将输入数据过滤为领域无关的表示，一个生成模块，用于促进知识迁移，以及一个求解模块，用于应用过滤后的可迁移知识来解决查询。 我们解决了连续领域自适应中的两个基本场景，展示了我们所提出方法在实际应用中的有效性。 显示英文摘要

摘要： Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.