• Corpus ID: 239016330

TLDR: Twin Learning for Dimensionality Reduction

@article{Kalantidis2021TLDRTL,
  title={TLDR: Twin Learning for Dimensionality Reduction},
  author={Yannis Kalantidis and Carlos Lassance and Jon Almaz{\'a}n and Diane Larlus},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09455}
}
Figure 1: Overview of the proposed TLDR, a dimensionality reduction method. Given a set of feature vectors in a generic input space, we use nearest neighbors to define a set of feature pairs whose proximity we want to preserve. We then learn a dimensionality-reduction function (the encoder) by encouraging neighbors in the input space to have similar representations. We learn it jointly with an auxiliary projector that produces high dimensional representations, where we compute the Barlow Twins… 

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