Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications. (arXiv:2209.06855v1 [cs.CV])

As input distributions evolve over a mission lifetime, maintaining
performance of learning-based models becomes challenging. This paper presents a
framework to incrementally retrain a model by selecting a subset of test inputs
to label, which allows the model to adapt to changing input distributions.
Algorithms within this framework are evaluated based on (1) model performance
throughout mission lifetime and (2) cumulative costs associated with labeling
and model retraining. We provide an open-source benchmark of a satellite pose
estimation model trained …

aerospace applications arxiv data data lifecycle management