Semi-supervised Adaptive Learning Across Domains
salad is a library to easily setup experiments using the current
state-of-the art techniques in domain adaptation. It features several of
recent approaches, with the goal of being able to run fair comparisons
between algorithms and transfer them to real-world use cases. The
toolbox is under active development and will extended when new
approaches are published.
Contribute and explore the code on Github. For commonly asked questions, head to our FAQ.
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/
directory.
Code for reproducing these results can be found in the scripts/ directory.
Usage is outlined below.
Requirements can be found in requirement.txt and can be installed
via
pip install -r requirements.txtInstall the package (recommended) via
pip install torch-saladFor the latest development version, install via
pip install git+https://github.com/domainadaptation/saladAlong 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) that can be easily run with the provided example script.
VADA (
VADASolver), arxiv:1802.08735$ python scripts/train_digits.py --source svhn --target mnist --vada
Domain Adversarial Training (
DANNSolver), jmlr:v17/15-239.html$ python scripts/train_digits.py --source svhn --target mnist --dann
Associative Domain Adaptation (
AssociativeSolver), arxiv:1708.00938$ python scripts/train_digits.py --source svhn --target mnist --assoc
Deep Correlation Alignment
$ python scripts/train_digits.py --source svhn --target mnist --coral
Self-Ensembling for Visual Domain Adaptation (
SelfEnsemblingSolver) arxiv:1706.05208$ python scripts/train_digits.py --source svhn --target mnist --teach
Adversarial Dropout Regularization (
AdversarialDropoutSolver), arxiv.org:1711.01575$ python scripts/train_digits.py --source svhn --target mnist --adv
Examples (already refer to the examples/ subfolder) soon to be added for:
- 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
To get started, the scripts/ directory contains several python scripts
for both running replication studies on digit benchmarks and studies on
a different dataset (toy example: adaptation to noisy images).
$ cd scripts
$ python train_digits.py --log ./log --teach --source svhn --target mnistRefer to the help pages for all options:
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
The chosen abstraction style organizes experiments into a subclass of
Solver.
As a quick MNIST experiment:
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):
passFor a simple tasks as MNIST, the code is quite long compared to other PyTorch examples TODO.
Legend: Implemented (✓), Under Construction (🚧)
- Digits: MNIST ↔ SVHN ↔ USPS ↔ SYNTH (✓)
- VisDA 2018 Openset and Detection (✓)
- Synthetic (GAN) ↔ Real (🚧)
- CIFAR ↔ STL (🚧)
- ImageNet to iCubWorld (🚧)
- White Noise ↔ Gratings ↔ Natural Images (🚧)
- Deep Lab Cut Tracking (🚧)
If you use salad in your publications, please cite
@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:
Further transfer learning ressources:
Maintained by Steffen Schneider. Work is part of my thesis project at the Bethge Lab. This README is also available as a webpage at salad.domainadaptation.org. We welcome issues and pull requests to the official github repository.