Learning invariance through imitation

@article{Taylor2011LearningIT,
  title={Learning invariance through imitation},
  author={Graham W. Taylor and Ian Spiro and Christoph Bregler and Rob Fergus},
  journal={CVPR 2011},
  year={2011},
  pages={2729-2736}
}
Supervised methods for learning an embedding aim to map high-dimensional images to a space in which perceptually similar observations have high measurable similarity. Most approaches rely on binary similarity, typically defined by class membership where labels are expensive to obtain and/or difficult to define. In this paper we propose crowd-sourcing similar images by soliciting human imitations. We exploit temporal coherence in video to generate additional pairwise graded similarities between… CONTINUE READING
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Retrieval performance. DCG@K on the test set vs. # of weight updates for various learned 32D embeddings of the One Frame of Fame dataset. Pixel-based matching performance is well below the curves

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