Deep hybrid networks with good out-of-sample object recognition

@article{Ghifary2014DeepHN,
  title={Deep hybrid networks with good out-of-sample object recognition},
  author={Muhammad Ghifary and W. Kleijn and M. Zhang},
  journal={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2014},
  pages={5437-5441}
}
We introduce Deep Hybrid Networks that are robust to the recognition of out-of-sample objects, i.e., ones that are drawn from a different probability distribution from the training data distribution. The networks are based on a particular combination of an auto-encoder and stacked Restricted Boltzmann Machines (RBMs). The autoencoder is used to extract sparse features, which are expected to be noise invariant in the observations. The stacked RBMs then observe the sparse features as inputs to… Expand
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