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Code to accompany ICML 2018 paper
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README.md

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

A pytorch implementation of CyCADA.

If you use this code in your research please consider citing

@inproceedings{Hoffman_cycada2017,
       authors = {Judy Hoffman and Eric Tzeng and Taesung Park and Jun-Yan Zhu,
             and Phillip Isola and Kate Saenko and Alexei A. Efros and Trevor Darrell},
       title = {CyCADA: Cycle Consistent Adversarial Domain Adaptation},
       booktitle = {International Conference on Machine Learning (ICML)},
       year = 2018
}

Setup

  • Check out the repo (recursively will also checkout the CyCADA fork of the CycleGAN repo).
    git clone --recursive https://github.com/jhoffman/cycada_release.git cycada
  • Install python requirements
    • pip install -r requirements.txt

Train image adaptation only (digits)

  • Image adaptation builds on the work on CycleGAN. The submodule in this repo is a fork which also includes the semantic consistency loss.
  • Pre-trained image results for digits may be downloaded here
  • Producing SVHN as MNIST
    • For an example of how to train image adaptation on SVHN->MNIST, see cyclegan/train_cycada.sh. From inside the cyclegan subfolder run train_cycada.sh.
    • The snapshots will be stored in cyclegan/cycada_svhn2mnist_noIdentity. Inside test_cycada.sh set the epoch value to the epoch you wish to use and then run the script to generate 50 transformed images (to preview quickly) or run test_cycada.sh all to generate the full ~73K SVHN images as MNIST digits.
    • Results are stored inside cyclegan/results/cycada_svhn2mnist_noIdentity/train_75/images.
    • Note we use a dataset of mnist_svhn and for this experiment run in the reverse direction (BtoA), so the source (SVHN) images translated to look like MNIST digits will be stored as [label]_[imageId]_fake_B.png. Hence when images from this directory will be loaded later we will only images which match that naming convention.

Train feature adaptation only (digits)

  • The main script for feature adaptation can be found inside scripts/train_adda.py
  • Modify the data directory you which stores all digit datasets (or where they will be downloaded)

Train feature adaptation following image adaptation

  • Use the feature space adapt code with the data and models from image adaptation
  • For example: to train for the SVHN to MNIST shift, set src = 'svhn2mnist' and tgt = 'mnist' inside scripts/train_adda.py
  • Either download the relevant images above or run image space adaptation code and extract transferred images

Train Feature Adaptation for Semantic Segmentation

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