论文标题
连续域的适应性，具有变化域 - 不可思议的功能重播
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
论文作者
论文摘要
在非平稳环境中学习是机器学习中最大的挑战之一。非平稳性可能是由任务漂移引起的，即标签的条件分布在给定输入数据的条件分布中或域漂移，即输入数据的边际分布中的漂移。本文旨在在连续域适应的背景下应对这一挑战，在这种情况下，需要模型来学习适合于非平稳环境中新领域的新任务，同时维持先前学习的知识。为了处理这两种漂移，我们提出了各种域 - 不可思议的功能重播，该方法由三个组件组成：将输入数据过滤到域 - 不可能表示的推理模块中，一种促进知识转移的生成模块以及一种应用过滤和转移知识的求解模块来求解Queries Queries Queries Queries。我们解决了连续域适应中的两个基本情况，证明了我们提出的实际使用方法的有效性。
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.
