  Active learning is a practical field of machine learning that automates the
process of selecting which data to label. Current methods are effective in
reducing the burden of data labeling but are heavily model-reliant. This has
led to the inability of sampled data to be transferred to new models as well as
issues with sampling bias. Both issues are of crucial concern in machine
learning deployment. We propose active learning methods utilizing combinatorial
coverage to overcome these issues. The proposed methods are data-centric, as
opposed to model-centric, and through our experiments we show that the
inclusion of coverage in active learning leads to sampling data that tends to
be the best in transferring to better performing models and has a competitive
sampling bias compared to benchmark methods.
