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As input distributions evolve over a mission lifetime, maintainingperformance of learning-based models becomes challenging. This paper presents aframework to incrementally retrain a model by selecting a subset of test inputsto label, which allows the model to adapt to changing input distributions.Algorithms within this framework are evaluated based on (1) model performancethroughout mission lifetime and (2) cumulative costs associated with labelingand model retraining. We provide an open-source benchmark of a satellite poseestimation model trained on images of a satellite in space and deployed innovel scenarios (e.g., different backgrounds or misbehaving pixels), wherealgorithms are evaluated on their ability to maintain high performance byretraining on a subset of inputs. We also propose a novel algorithm to select adiverse subset of inputs for labeling, by characterizing the information gainfrom an input using Bayesian uncertainty quantification and choosing a subsetthat maximizes collective information gain using concepts from batch activelearning. We show that our algorithm outperforms others on the benchmark, e.g.,achieves comparable performance to an algorithm that labels 100% of inputs,while only labeling 50% of inputs, resulting in low costs and high performanceover the mission lifetime.

Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications