  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.
