Unsupervised Representation Learning by Discovering Reliable Image Relations

@article{Milbich2020UnsupervisedRL,
  title={Unsupervised Representation Learning by Discovering Reliable Image Relations},
  author={Timo Milbich and Omair Ghori and F. Diego and Bjorn Ommer},
  journal={Pattern Recognit.},
  year={2020},
  volume={102},
  pages={107107}
}
  • Timo Milbich, Omair Ghori, +1 author Bjorn Ommer
  • Published 2020
  • Computer Science
  • Pattern Recognit.
  • Learning robust representations that allow to reliably establish relations between images is of paramount importance for virtually all of computer vision. Annotating the quadratic number of pairwise relations between training images is simply not feasible, while unsupervised inference is prone to noise, thus leaving the vast majority of these relations to be unreliable. To nevertheless find those relations which can be reliably utilized for learning, we follow a divide-and-conquer strategy: We… CONTINUE READING
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