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Recent works on no-reference image quality assessment (NR-IQA) have reported good performance for various datasets. However, they suffer from significant performance drops in cross-dataset evaluations which indicates poor generalization power. We propose a Siamese architecture and training procedures for cross-dataset deep NR-IQA that achieves clearly better performance. Moreover, we show that the architecture can be further boosted by i) pre-training with a large aesthetics dataset and ii)doi:10.1109/iccvw.2019.00485 dblp:conf/iccvw/YangPK19 fatcat:72ruk7bhmjcjxabnlbjtfcfrna