  Recently, the group maximum differentiation competition (gMAD) has been used
to improve blind image quality assessment (BIQA) models, with the help of
full-reference metrics. When applying this type of approach to troubleshoot
"best-performing" BIQA models in the wild, we are faced with a practical
challenge: it is highly nontrivial to obtain stronger competing models for
efficient failure-spotting. Inspired by recent findings that difficult samples
of deep models may be exposed through network pruning, we construct a set of
"self-competitors," as random ensembles of pruned versions of the target model
to be improved. Diverse failures can then be efficiently identified via
self-gMAD competition. Next, we fine-tune both the target and its pruned
variants on the human-rated gMAD set. This allows all models to learn from
their respective failures, preparing themselves for the next round of self-gMAD
competition. Experimental results demonstrate that our method efficiently
troubleshoots BIQA models in the wild with improved generalizability.
