Our Research
On 10th September 2019 Raiders of the Lost Art was submitted to arXiv as a pre-print for our submission to NeurIPS workshop on Machine Learning for Creativity and Design 2019. On 20th September 2019 our work was picked up by MIT Technology Review and subsequently gained worldwide press coverage.
Since then we have worked under the guidance of the world’s leading experts in art connoisseurship and computer vision, such as Dr. David G. Stork, and have subsequently presented several works at Electronic Imaging. Our list of publications include:
Bourached, et al. (2023), Style transfer for improved visualization of underdrawings and ghost paintings: An application to a work by Vincent van Gogh.
Eriksson, et al. (2023), Recovery of lost artworks by deep neural networks: Motivations, methodology, and proof-of-concept simulations..
Kell, et al. (2022), Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks.
Bourached, et al. (2021), Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks.
Cann, et al. (2021), Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks.
Stork, et al. (2021), Computational identification of significant actors in paintings through symbols and attribute.
Bourached and Cann (2019), Raiders of the Lost Art.