  Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers to
allow them to operate in complex, high-dimensional, non-linear, and dynamically
changing environments. Due to the complexity of these environments, DNN
classifiers may output misclassifications during operation when they face
domains not identified during development. Removing a system from operation for
retraining becomes impractical as the number of such AS increases. To increase
AS reliability and overcome this limitation, DNN classifiers need to have the
ability to adapt during operation when faced with different operational domains
using a few samples (e.g. 2 to 100 samples). However, retraining DNNs on a few
samples is known to cause catastrophic forgetting and poor generalisation. In
this paper, we introduce Dynamic Incremental Regularised Adaptation (DIRA), an
approach for dynamic operational domain adaption of DNNs using regularisation
techniques. We show that DIRA improves on the problem of forgetting and
achieves strong gains in performance when retraining using a few samples from
the target domain. Our approach shows improvements on different image
classification benchmarks aimed at evaluating robustness to distribution shifts
(e.g.CIFAR-10C/100C, ImageNet-C), and produces state-of-the-art performance in
comparison with other methods from the literature.
