Self-supervised learning using view synthesis

Self-supervised learning using view synthesis

Recently, there has been great progress in synthesizing novel views on objects [1,2,3,4]. At the same time, augmentations have been found to be crucial component in self-supervised learning systems [5]. In such systems, generated views could serve as a form of augmentation which do not shift the intensity distribution of the image (other than e.g. color augmentation).


[1] Wiles et al.: SynSin: End-to-end View Synthesis from a Single Image
[2] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
[5] T. Chen et al.: A simple framework for contrastive learning of visual representations


  • Good mathematical understanding (in particular statistics and linear algebra)
  • Python programming
  • Experience in machine learning


Timo L├╝ddecke

Neural Data Science Group
Institute of Computer Science
University of Goettingen