🥗 salad

S\ emi-supervised A\ daptive L\ earning A\ cross D\ omains

.. figure:: img/domainshift.png :alt:

salad

Contribute and explore the code on

Github 

FAQ 

Check out robusta, our new library for domain adaptation and robustness evaluation on ImageNet scale: https://github.com/bethgelab/robustness

📊 Benchmarking Results

One of salad's purposes is to constantly track the state of the art of a variety of domain adaptation algorithms. The latest results can be reproduced by the files in the

scripts/

.. figure:: img/benchmarks.svg :alt:

Code for reproducing these results can be found in the

scripts/

💻 Installation

Requirements can be found in

requirement.txt

.. code:: bash

pip install -r requirements.txt

Install the package (recommended) via

.. code:: bash

pip install torch-salad

For the latest development version, install via

.. code:: bash

pip install git+https://github.com/domainadaptation/salad

📚 Using this library

Along with the implementation of domain adaptation routines, this library comprises code to easily set up deep learning experiments in general.

The toolbox currently implements the following techniques (in

salad.solver

• VADA (

  VADASolver

  ),

  arxiv:1802.08735 

  __

.. code:: bash

    $ python scripts/train_digits.py --source svhn --target mnist  --vada

• Domain Adversarial Training (

  DANNSolver

  ),

  jmlr:v17/15-239.html 

  __

.. code:: bash

    $ python scripts/train_digits.py --source svhn --target mnist  --dann  

• Associative Domain Adaptation (

  AssociativeSolver

  ),

  arxiv:1708.00938 

  __

.. code:: bash

    $ python scripts/train_digits.py --source svhn --target mnist  --assoc  

• Deep Correlation Alignment

.. code:: bash

$ python scripts/train_digits.py --source svhn --target mnist  --coral  

• Self-Ensembling for Visual Domain Adaptation (

  SelfEnsemblingSolver

  )

  arxiv:1706.05208 

  __

.. code:: bash

   $ python scripts/train_digits.py --source svhn --target mnist    --teach

• Adversarial Dropout Regularization (

  AdversarialDropoutSolver

  ),

  arxiv.org:1711.01575 

  __

.. code:: bash

   $ python scripts/train_digits.py --source svhn --target mnist  --adv  

Examples (already refer to the

examples/

• Generalizing Across Domains via Cross-Gradient Training (

  CrossGradSolver

  ),

  arxiv:1708.00938 

  __ Example coming soon!

• DIRT-T (

  DIRTTSolver

  ),

  arxiv:1802.08735 

  __

Implements the following features (in

salad.layers

• Weights Ensembling using Exponential Moving Averages or Stored Weights
• WalkerLoss and Visit Loss (

  arxiv:1708.00938 

  __)
• Virtual Adversarial Training (

  arxiv:1704.03976 

  __)

Coming soon:

• Deep Joint Optimal Transport (

  DJDOTSolver

  ),

  arxiv:1803.10081 

  __
• Translation based approaches

Quick Start ~~~~~~~~~~~

To get started, the

scripts/

.. code:: bash

$ cd scripts
$ python train_digits.py --log ./log --teach --source svhn --target mnist

Refer to the help pages for all options:

.. code::

usage: train_digits.py [-h] [--gpu GPU] [--cpu] [--njobs NJOBS] [--log LOG]
                    [--epochs EPOCHS] [--checkpoint CHECKPOINT]
                    [--learningrate LEARNINGRATE] [--dryrun]
                    [--source {mnist,svhn,usps,synth,synth-small}]
                    [--target {mnist,svhn,usps,synth,synth-small}]
                    [--sourcebatch SOURCEBATCH] [--targetbatch TARGETBATCH]
                    [--seed SEED] [--print] [--null] [--adv] [--vada]
                    [--dann] [--assoc] [--coral] [--teach]

Domain Adaptation Comparision and Reproduction Study
optional arguments:
-h, --help            show this help message and exit
--gpu GPU             Specify GPU
--cpu                 Use CPU Training
--njobs NJOBS         Number of processes per dataloader
--log LOG             Log directory. Will be created if non-existing
--epochs EPOCHS       Number of Epochs (Full passes through the unsupervised
                        training set)
--checkpoint CHECKPOINT
                        Checkpoint path
--learningrate LEARNINGRATE
                        Learning rate for Adam. Defaults to Karpathy's
                        constant ;-)
--dryrun              Perform a test run, without actually training a
                        network.
--source {mnist,svhn,usps,synth,synth-small}
                        Source Dataset. Choose mnist or svhn
--target {mnist,svhn,usps,synth,synth-small}
                        Target Dataset. Choose mnist or svhn
--sourcebatch SOURCEBATCH
                        Batch size of Source
--targetbatch TARGETBATCH
                        Batch size of Target
--seed SEED           Random Seed
--print
--null
--adv                 Train a model with Adversarial Domain Regularization
--vada                Train a model with Virtual Adversarial Domain
                        Adaptation
--dann                Train a model with Domain Adversarial Training
--assoc               Train a model with Associative Domain Adaptation
--coral               Train a model with Deep Correlation Alignment
--teach               Train a model with Self-Ensembling

Reasons for using solver abstractions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The chosen abstraction style organizes experiments into a subclass of

Solver

Quickstart: MNIST Experiment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

As a quick MNIST experiment:

.. code:: python

from salad.solvers import Solver

class MNISTSolver(Solver):
def __init__(self, model, dataset, **kwargs):

    self.model = model
    super().__init__(dataset, **kwargs)

def _init_optims(self, lr = 1e-4, **kwargs):
    super()._init_optims(**kwargs)

    opt = torch.optim.Adam(self.model.parameters(), lr = lr)
    self.register_optimizer(opt)

def _init_losses(self):
    pass

For a simple tasks as MNIST, the code is quite long compared to other PyTorch examples

TODO 

💡 Domain Adaptation Problems

Legend: Implemented (✓), Under Construction (🚧)

📷 Vision ~~~~~~~~~

• Digits: MNIST ↔ SVHN ↔ USPS ↔ SYNTH (✓)

• VisDA 2018 Openset and Detection 

  __ (✓)
• Synthetic (GAN) ↔ Real (🚧)
• CIFAR ↔ STL (🚧)
• ImageNet to

  iCubWorld 

  __ (🚧)

🎤 Audio ~~~~~~~~

• Mozilla Common Voice Dataset 

  __ (🚧)

፨ Neuroscience ~~~~~~~~~~~~~~

• White Noise ↔ Gratings ↔ Natural Images (🚧)

• Deep Lab Cut Tracking 

  __ (🚧)

🔗 References

If you use salad in your publications, please cite

.. code:: bibtex

@misc{schneider2018salad, title={Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains}, author={Schneider, Steffen and Ecker, Alexander S. and Macke, Jakob H. and Bethge, Matthias}, year={2018}, url={https://openreview.net/forum?id=S1lTifykqm} }

along with the references to the original papers that are implemented here.

Part of the code in this repository is inspired or borrowed from original implementations, especially:

• https://github.com/Britefury/self-ensemble-visual-domain-adapt
• https://github.com/Britefury/self-ensemble-visual-domain-adapt-photo/
• https://github.com/RuiShu/dirt-t
• https://github.com/gpascualg/CrossGrad
• https://github.com/stes/torch-associative
• https://github.com/haeusser/learning_by_association
• https://mil-tokyo.github.io/adr_da/

Excellent list of domain adaptation ressources:

• https://github.com/artix41/awesome-transfer-learning

Further transfer learning ressources:

• http://transferlearning.xyz

👤 Contact

Maintained by

Steffen Schneider 

Bethge Lab 

salad.domainadaptation.org 

to the official github
repository