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.

通过使用 Encoder-Adaptor-Reconfigurator（EAR）框架，本文提出了一种高效的持续学习方法，用于处理领域转移下的非平稳数据分布以及新的数据，该框架利用深度神经网络（DNN）特征编码器和浅层网络进行训练，通过组合 DNN 和超维计算（HDC）检测新数据是否在分布之外，使用零样本神经架构搜索（ZS-NAS）识别低参数神经适配器以适应分布之外的数据，并通过根据需要动态增长神经架构，并通过适当的适配器和重构器来处理领域增量和类增量的持续学习，以最小化对之前任务的灾难性遗忘。通过在几个基准数据集上系统评估我们的方法，并与最先进的 ODD 检测和零样本 NAS 算法进行对比，证明了我们方法的强大性能。