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Chainer Implementation of the paper "Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation" (DRCN).

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chainer-DRCN

Chainer Implementation of the paper "Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation" (DRCN).

Usage

python train.py

Optional arguments:

  -h, --help            show this help message and exit
  --device  # GPU to use
  --max_iter  # maximum iteration for training (epoch)
  --interval  # interval to evaluate/snapshot the model
  --batchsize  # mini-batch size
  --lr learning  # rate
  --out  # directory for experiment outputs
  --unit {iteration,epoch}
  --noise {no_noise,impulse,gaussian}  # whether to add noise to target domain images
  --source_only  # if this is True (1), train the model without using target domain data (without adaptation)

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Chainer Implementation of the paper "Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation" (DRCN).

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