Transforming Auto-Encoders

@inproceedings{Hinton2011TransformingA,
  title={Transforming Auto-Encoders},
  author={Geoffrey E. Hinton and Alex Krizhevsky and Sida D. Wang},
  booktitle={ICANN},
  year={2011}
}
The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a whole vector of outputs including an explicit representation of the pose of the feature. We show how neural networks can be used to learn features that output a whole vector of instantiation parameters and we argue that this… CONTINUE READING
Highly Influential
This paper has highly influenced 10 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 235 citations. REVIEW CITATIONS
161 Citations
12 References
Similar Papers

Citations

Publications citing this paper.

235 Citations

050100'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 235 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…