Originally a subfield of artificial intelligence, machine learning has now established itself as an important research direction. The main purpose of machine learning is to develop mathematical models by learning from data. Machine learning plays a crucial role in many research areas and is part of an overall process in many industrial directions. In particular, Deep learning systems serve as a supportive instrument for artists to experiment and explore new, creative avenues. Examples include style transfer in painting, music production, or even the creation of poetry. The workshop also aims to explore artificial creativity and to underpin this new artistic form by developing appropriate tools. The exchange with colleagues from the arts is important here and is explicitly supported with this workshop.

This workshop is about the concrete contribution of Machine Learning to the field of Arts. Participants will also present their own vision through numerous works of their own, such as programming works or substantial research overviews, thus motivating Machine Learning as an important component of modern AI for a new kind of creativity.

Topics include:

Machine learning and photo style transfer, Machine learning and music composition, Machine learning for text generation, Machine learning for the performing arts or any other field related to the Arts




Begin: 14h00, MSA Room 3.110

End :   18h30


14h00 Welcome

CATEGORY 1: TEXT ( 14h05 – 14h50 )

  • 14h05    Hugo Kolander: Review of poem/poesy generation
  • #14h20    Salijona Dyrmishi, Tiezhu Sun: How effective are  text-to-image models to generate animated stories: An automated vs human assisted approach
  • 14h35 Discussion 10 minutes

CATEGORY 2: PAINTING ( 14h45 – 16h00 )

  • 14h45 Greeshma Seetharaman Menon: Photo style transfer using Machine learning
  • 15h00 Somayeh Khazaei, Alexis Ciarrone, Alessandro Giffra: CycleGANs, a computer-generated art demonstration
  • #15h15 Charline Baur, Abir El Beji: Style transfer learning model which will mix two different styles of art
  • #15h30 Xueqi Dang, Yinghua Li: AI-driven apps for arts
  • 15h45 Discussion

16h00 BREAK 10 minutes

CATEGORY 3: MUSIC ( 16h10 – 17h40 )

  • 16h10 Pedro Jesús  Ruiz Jiménez: AI composed music vs. Artist composed music – Can we distinguish them?
  • 16h25 Navid Vafaei: Machine learning for music generation
  • 16h40 Astley Santos, Hamza Dovutbekov: Music with AI: Retrospective
  • 16h55 Meliane Angele: AI and Music
  • 17h10 Ciaran Hagen, David Holbrechts: Can AI rap?
  • 17h25 Discussion 10 minutes

CATEGORY 4: GENERAL ( 17h35 – 18h35 )

  • 17h35 Maria Zhekova, Cem Güvel: What is Art ? Is AI Art?
  • 17h50 Tom Deckenbrunnen, Alexander Philipp Hennen, Vlada Khomenko: AI as a tool of an artist
  • 18h05 Nima Gozalpour, Akhil Satheesh: replication of “Play as you like”
  • 18h20 Discussion 10 minutes

18h30 Closing


PS: 3 talks marked with a “#” are PhD candidates’ presentations