Improving Color Mixture Predictions in Ceramics using Data-centric Deep Learning

Author:

Souper Tomas1ORCID,Morgado Ana C.1ORCID,Marques Ana2ORCID,Silva Inês2ORCID,Rosado Luís1ORCID

Affiliation:

1. Fraunhofer Portugal AICOS, Portugal

2. Matcerâmica - Fabrico de Louça S.A, Portugal

Publisher

ACM

Reference16 articles.

1. JM Bishop , MJ Bushnell , A Usher , and S Westland . 1991. Neural networks in the colour industry . In Applications of Artificial Intelligence in Engineering VI. Springer , 423–434. JM Bishop, MJ Bushnell, A Usher, and S Westland. 1991. Neural networks in the colour industry. In Applications of Artificial Intelligence in Engineering VI. Springer, 423–434.

2. Application of neural networks to computer recipe prediction

3. Color prediction in ceramic enamels using the Kubelka Kunk Model

4. Developments in MLflow

5. Mei-Yun Chen , Ya-Bo Huang , Sheng-Ping Chang , and Ming Ouhyoung . 2019. Prediction Model for Semitransparent Watercolor Pigment Mixtures Using Deep Learning with a Dataset of Transmittance and Reflectance. arXiv preprint arXiv:1904.00275 ( 2019 ). Mei-Yun Chen, Ya-Bo Huang, Sheng-Ping Chang, and Ming Ouhyoung. 2019. Prediction Model for Semitransparent Watercolor Pigment Mixtures Using Deep Learning with a Dataset of Transmittance and Reflectance. arXiv preprint arXiv:1904.00275 (2019).

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