Department of Mathematics

Student Applied Math Seminar

  •  Liping Yin, MSU
  •  Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss
  •  11/21/2022
  •  1:00 PM - 2:00 PM
  •  C117 Wells Hall (Virtual Meeting Link)
  •  Craig Gross (grosscra@msu.edu)

In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Thus, we propose a new set of statistics for exemplar based texture synthesis based on Sliced Wasserstein Loss and create a multi-scale algorithm to synthesize textures without any regularization terms or user-added spatial tags. Lastly, we study the ability of our proposed algorithm to capture long range constraints in images and compare our results to other exemplar-based neural texture synthesis algorithms. $\\$ This will be a hybrid seminar and take place in C117 Wells Hall and via Zoom at https://msu.zoom.us/j/99426648081?pwd=ZEljM3BPUXg2MjVUMVM5TnlzK2NQZz09 .

 

Contact

Department of Mathematics
Michigan State University
619 Red Cedar Road
C212 Wells Hall
East Lansing, MI 48824

Phone: (517) 353-0844
Fax: (517) 432-1562

College of Natural Science