Troubleshooting Blind Image Quality Models in the Wild

• URL: http://arxiv.org/abs/2105.06747v1
• Date: Fri, 14 May 2021 10:10:48 GMT
• Title: Troubleshooting Blind Image Quality Models in the Wild
• Authors: Zhihua Wang and Haotao Wang and Tianlong Chen and Zhangyang Wang and Kede Ma
• Abstract summary: Group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models. 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.
• Score: 99.96661607178677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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