Sampling Matters in Deep Embedding Learning

@article{Manmatha2017SamplingMI,
  title={Sampling Matters in Deep Embedding Learning},
  author={R. Manmatha and Chao-Yuan Wu and Alex Smola and Philipp Kr{\"a}henb{\"u}hl},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2859-2867}
}
Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling… Expand
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