  In this paper, we propose a predictive quantifier to estimate the retraining
cost of a trained model in distribution shifts. The proposed Aggregated
Representation Measure (ARM) quantifies the change in the model's
representation from the old to new data distribution. It provides, before
actually retraining the model, a single concise index of resources - epochs,
energy, and carbon emissions - required for the retraining. This enables reuse
of a model with a much lower cost than training a new model from scratch. The
experimental results indicate that ARM reasonably predicts retraining costs for
varying noise intensities and enables comparisons among multiple model
architectures to determine the most cost-effective and sustainable option.
