Many traditional computer vision algorithms generate realistic images by requiring that each patch in the generated image be similar to a patch in a training image and vice versa. Recently, this classical approach has been replaced by adversarial training with a patch discriminator. The adversarial approach avoids the computational burden of finding nearest neighbors of patches but often requires very long training times and may fail to match the distribution of patches. In this paper we leverage the Sliced Wasserstein Distance to develop an algorithm that explicitly and efficiently minimizes the distance between patch distributions in two images. Our method is conceptually simple, requires no training and can be implemented in a few lines of codes. On a number of image generation tasks we show that our results are often superior to single-image-GANs, and can generate high quality images in a few seconds. Our implementation is publicly available at https://github.com/ariel415el/GPDM.
|Original language||American English|
|Title of host publication||Computer Vision – ECCV 2022 - 17th European Conference, Proceedings|
|Editors||Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||17|
|State||Published - 2022|
|Event||17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel|
Duration: 23 Oct 2022 → 27 Oct 2022
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||17th European Conference on Computer Vision, ECCV 2022|
|Period||23/10/22 → 27/10/22|
Bibliographical noteFunding Information:
Acknowledgements. Support from the Israeli Ministry of Science and Technology and the Gatsby Foundation is gratefully acknowledged. We also thank the authors of  for answering our question about their method.
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.