Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences

@article{Harvey2015SemisupervisedLW,
  title={Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences},
  author={F{\'e}lix G. Harvey and Christopher Joseph Pal},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.06653}
}
Recent work on sequence to sequence translation using Recurrent Neural Networks (RNNs) based on Long Short Term Memory (LSTM) architectures has shown great potential for learning useful representations of sequential data. A one-to-many encoder-decoder(s) scheme allows for a single encoder to provide representations serving multiple purposes. In our case, we present an LSTM encoder network able to produce representations used by two decoders: one that reconstructs, and one that classifies if the… CONTINUE READING

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