Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed on real devices, data distributions often shift over time due to changes in environmental factors, sensor characteristics, and task-of-interest. While it is possible to have a human-in-the-loop to monitor for distribution shifts and engineer new architectures in response to these shifts, such a setup is not cost-effective. Instead, non-stationary automated ML (AutoML) models are needed. This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts. The EAR framework uses a fixed deep neural network (DNN) feature encoder and trains shallow networks on top of the encoder to handle novel data. The EAR framework is capable of 1) detecting when new data is out-of-distribution (OOD) by combining DNNs with hyperdimensional computing (HDC), 2) identifying low-parameter neural adaptors to adapt the model to the OOD data using zero-shot neural architecture search (ZS-NAS), and 3) minimizing catastrophic forgetting on previous tasks by progressively growing the neural architecture as needed and dynamically routing data through the appropriate adaptors and reconfigurators for handling domain-incremental and class-incremental continual learning. We systematically evaluate our approach on several benchmark datasets for domain adaptation and demonstrate strong performance compared to state-of-the-art algorithms for OOD detection and few-/zero-shot NAS.

翻译：大多数机器学习系统假设训练和部署期间数据分布保持静态且匹配，但这往往是一个错误假设。当机器学习模型部署到真实设备上时，由于环境因素、传感器特性和目标任务的变化，数据分布通常会随时间发生偏移。虽然可以通过引入人工干预来监控分布偏移并针对这些偏移设计新架构，但这种方案成本效益较低。因此，需要非平稳自动机器学习模型来应对这一挑战。本文提出编码器-适配器-重构器框架，用于在域偏移场景下实现高效的持续学习。该框架采用固定深度神经网络特征编码器，并在编码器之上训练浅层网络以处理新颖数据。EAR框架具备三项核心能力：1）通过将深度神经网络与超维度计算结合，检测新数据是否属于分布外样本；2）利用零样本神经架构搜索识别低参数量的神经适配器，使模型适应OOD数据；3）通过按需渐进式扩展神经架构并动态路由数据至相应适配器和重构器，最小化先前任务上的灾难性遗忘，从而支持域增量学习和类增量学习。我们在多个基准数据集上系统评估了该方法在域适应任务中的表现，结果表明其在OOD检测及少样本/零样本神经架构搜索方面相较于当前最优算法具有显著优势。