02529nas a2200457   4500000000100000000000100001000000100002008004100003260000800044653002500052653001900077653001400096653002400110653001900134653003600153653001800189653003500207653001900242653002500261653003400286653002400320653001400344653003300358653003900391653002600430653001000456653002700466100001600493700001200509700001600521700001600537700001800553700001000571700001400581245011500595856014600710300001400856490000700870520117400877022002002051       2019                            d  cdec10aAdversarial networks10aArts computing10aCantonese10aCantonese porcelain10aClassification10aClassification (of information)10aCreative arts10aGenerative adversarial network10aHybrid systems10aImage classification10aIntangible cultural heritages10aLogistic regression10aPorcelain10aPrincipal component analysis10aReceiver operating characteristics10aStyle identifications10aTP75110aTraditional porcelains1 aSteven Chen1 aHui Cui1 aMing-han Du1 aTie-ming Fu1 aXiao-hong Sun1 aYi Ji1 aHenry Duh00aCantonese porcelain classification and image synthesis by ensemble learning and generative adversarial network  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079119784&doi=10.1631%2fFITEE.1900399&partnerID=40&md5=6c49358e7adf47fb240dc3f65984c93d  a1632-16430 v203 aAccurate recognition of modern and traditional porcelain styles is a challenging issue in Cantonese porcelain management due to the large variety and complex elements and patterns. We propose a hybrid system with porcelain style identification and image recreation modules. In the identification module, prediction of an unknown porcelain sample is obtained by logistic regression of ensembled neural networks of top-ranked design signatures, which are obtained by discriminative analysis and transformed features in principal components. The synthesis module is developed based on a conditional generative adversarial network, which enables users to provide a designed mask with porcelain elements to generate synthesized images of Cantonese porcelain. Experimental results of 603 Cantonese porcelain images demonstrate that the proposed model outperforms other methods relative to precision, recall, area under curve of receiver operating characteristic, and confusion matrix. Case studies on image creation indicate that the proposed system has the potential to engage the community in understanding Cantonese porcelain and promote this intangible cultural heritage.  a20959184 (ISSN)