Awesome Transfer Learning
-------------------------

A list of awesome papers and cool resources on transfer learning, domain
adaptation and domain-to-domain translation in general! As you will
notice, this list is currently mostly focused on domain adaptation (DA)
and domain-to-domain translation, but don't hesitate to suggest
resources in other subfields of transfer learning. I accept pull
requests.

Table of Contents
-----------------

-  `Tutorial and Blogs <#tutorials-and-blogs>`__
-  `Papers <#papers>`__
-  `Surveys <#surveys>`__
-  `Deep Transfer Learning <#deep-transfer-learning>`__

   -  `Fine-tuning approach <#fine-tuning-approach>`__
   -  `Feature extraction (embedding)
      approach <#feature-extraction-embedding-approach>`__
   -  `Policy transfer for RL <#policy-transfer-for-rl>`__
   -  `Few-shot transfer learning <#few-shot-transfer-learning>`__
   -  `Meta transfer learning <#meta-transfer-learning>`__
   -  `Applications <#applications>`__

-  `Unsupervised Domain Adaptation <#unsupervised-domain-adaptation>`__

   -  `Theory <#theory>`__
   -  `Adversarial methods <#adversarial-methods>`__
   -  `Optimal Transport <#optimal-transport>`__
   -  `Embedding methods <#embedding-methods>`__
   -  `Kernel methods <#kernel-methods>`__
   -  `Autoencoder approach <#autoencoder-approach>`__
   -  `Subspace learning <#subspace-learning>`__
   -  `Self-ensembling methods <#self-ensembling-methods>`__
   -  `Other <#other>`__

-  `Semi-supervised Domain
   Adaptation <#semi-supervised-domain-adaptation>`__

   -  `General methods <#general-methods>`__
   -  `Subspace learning <#subspace-learning>`__
   -  `Copulas methods <#copulas-methods>`__

-  `Few-shot Supervised Domain
   Adaptation <#few-shot-supervised-domain-adaptation>`__

   -  `Adversarial methods <#adversarial-methods>`__
   -  `Embedding methods <#embedding-methods>`__

-  `Applied Domain Adaptation <#applied-domain-adaptation>`__

   -  `Physics <#physics>`__

-  `Datasets <#datasets>`__
-  `Image-to-image <#image-to-image>`__
-  `Text-to-text <#text-to-text>`__
-  `Results <#results>`__
-  `Digits transfer <digits-transfer>`__
-  `Challenges <#challenges>`__
-  `Libraries <#libraries>`__

Tutorials and Blogs
-------------------

-  `Transfer Learning − Machine Learning's Next
   Frontier <http://ruder.io/transfer-learning/index.html>`__
-  `A Little Review of Domain Adaptation in
   2017 <https://artix41.github.io/static/domain-adaptation-in-2017/>`__

Papers
------

Papers are ordered by theme and inside each theme by publication date
(submission date for arXiv papers). If the network or algorithm is given
a name in a paper, this one is written in bold before the paper's name.

Surveys
~~~~~~~

-  `A Survey on Transfer
   Learning <https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf>`__
   (2009)
-  `Transfer Learning for Reinforcement Learning Domains: A
   Survey <http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf>`__
   (2009)
-  `A Survey of transfer
   learning <https://link.springer.com/article/10.1186/s40537-016-0043-6>`__
   (2016)
-  `Domain Adaptation for Visual Applications: A Comprehensive
   Survey <https://arxiv.org/pdf/1702.05374.pdf>`__ (2017)
-  `Deep Visual Domain Adaptation: A
   Survey <https://arxiv.org/pdf/1802.03601.pdf>`__ (2018)

Deep Transfer Learning
~~~~~~~~~~~~~~~~~~~~~~

Transfer of deep learning models.

Fine-tuning approach
^^^^^^^^^^^^^^^^^^^^

-  `Do Better ImageNet Models Transfer
   Better? <https://arxiv.org/pdf/1805.08974.pdf>`__ (2018)

Feature extraction (embedding) approach
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-  `CNN Features off-the-shelf: an Astounding Baseline for
   Recognition <https://www.cv-foundation.org//openaccess/content_cvpr_workshops_2014/W15/papers/Razavian_CNN_Features_Off-the-Shelf_2014_CVPR_paper.pdf>`__
   (2014)
-  `Taskonomy: Disentangling Task Transfer
   Learning <https://arxiv.org/pdf/1804.08328v1.pdf>`__ (2018)

Multi-task learning
^^^^^^^^^^^^^^^^^^^

-  `Learning without forgetting <https://arxiv.org/pdf/1606.09282>`__
   (2016)

Policy transfer for RL
^^^^^^^^^^^^^^^^^^^^^^

-  `Reinforcement Learning to Train Ms. Pac-Man Using Higher-order
   Action-relative
   Inputs <https://www.rug.nl/research/portal/files/19535198/MS_PACMAN_RL.pdf>`__
   (2013)

Few-shot transfer learning
^^^^^^^^^^^^^^^^^^^^^^^^^^

-  `Zero-Shot Transfer Learning for Event
   Extraction <https://arxiv.org/pdf/1707.01066.pdf>`__ (2017)
-  `Learning a Deep Embedding Model for Zero-Shot
   Learning <https://www.eecs.qmul.ac.uk/~sgg/papers/ZhangEtAl_CVPR2017.pdf>`__
   (2017)

Meta transfer learning
^^^^^^^^^^^^^^^^^^^^^^

-  `Transfer Learning via Learning to
   Transfer <http://proceedings.mlr.press/v80/wei18a/wei18a.pdf>`__
   (2018)

Applications
^^^^^^^^^^^^

Medical imaging:
''''''''''''''''

-  `Deep Convolutional Neural Networks forComputer-Aided Detection: CNN
   Architectures, Dataset Characteristics and Transfer
   Learning <https://arxiv.org/pdf/1602.03409>`__ (2016)
-  `Convolutional Neural Networks for Medical Image Analysis: Full
   Training or Fine Tuning? <https://arxiv.org/pdf/1706.00712.pdf>`__
   (2017)
-  `Comparison of deep transfer learning strategies for digital
   pathology <https://orbi.uliege.be/bitstreaom/2268/222511/1/mormont2018-comparison.pdf>`__
   (2018)

Robotics
''''''''

-  `A Deep Convolutional Neural Network for Location Recognition and
   Geometry Based
   Information <http://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/ICPRAM_CNN_LOCALIZATION_2018.pdf>`__
   (2018)

Unsupervised Domain Adaptation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Transfer between a source and a target domain. In unsupervised domain
adaptation, only the source domain can have labels.

Theory
^^^^^^

General
'''''''

-  `A theory of learning from different
   domains <http://www.alexkulesza.com/pubs/adapt_mlj10.pdf>`__ (2010)

Multi-source
''''''''''''

-  `Domain Adaptation with Multiple
   Sources <https://papers.nips.cc/paper/3550-domain-adaptation-with-multiple-sources.pdf>`__
   (2008)
-  `Algorithms and Theory for Multiple-Source
   Adaptation <https://arxiv.org/pdf/1805.08727.pdf>`__ (2018)

Adversarial methods
^^^^^^^^^^^^^^^^^^^

Learning a latent space
'''''''''''''''''''''''

-  **DANN**: `Domain-Adversarial Training of Neural
   Networks <https://arxiv.org/pdf/1505.07818.pdf>`__ (2015)
-  **JAN**: `Deep Transfer Learning with Joint Adaptation
   Networks <https://arxiv.org/pdf/1605.06636.pdf>`__ (2016)
-  **CoGAN**: `Coupled Generative Adversarial
   Networks <https://arxiv.org/pdf/1606.07536.pdf>`__ (2016)
-  **DRCN**: `Deep Reconstruction-Classification Networks for
   Unsupervised Domain
   Adaptation <https://arxiv.org/pdf/1607.03516.pdf>`__ (2016)
-  **DSN**: `Domain Separation
   Networks <https://arxiv.org/pdf/1608.06019.pdf>`__ (2016)
-  **ADDA**: `Adaptative Discriminative Domain
   Adaptation <https://arxiv.org/pdf/1702.05464.pdf>`__ (2017)
-  **GenToAdapt**: `Generate To Adapt: Aligning Domains using Generative
   Adversarial Networks <https://arxiv.org/pdf/1704.01705.pdf>`__ (2017)
-  **WDGRL**: `Wasserstein Distance Guided Representation Learning for
   Domain Adaptation <https://arxiv.org/pdf/1707.01217.pdf>`__ (2017)
-  **CyCADA**: `CyCADA: Cycle-Consistent Adversarial Domain
   Adaptation <http://proceedings.mlr.press/v80/hoffman18a/hoffman18a.pdf>`__
   (2017)
-  **DIRT-T**: `A DIRT-T Approach to Unsupervised Domain
   Adaptation <https://arxiv.org/pdf/1802.08735.pdf>`__ (2017)
-  **DupGAN**: `Duplex Generative Adversarial Network for Unsupervised
   Domain
   Adaptation <http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Duplex_Generative_Adversarial_CVPR_2018_paper.pdf>`__
   (2018)
-  **MSTN**: `Learning Semantic Representations for Unsupervised Domain
   Adaptation <http://proceedings.mlr.press/v80/xie18c/xie18c.pdf>`__
   (2018)

Image-to-Image translation
''''''''''''''''''''''''''

-  **DIAT**: `Deep Identity-aware Transfer of Facial
   Attributes <https://arxiv.org/pdf/1610.05586.pdf>`__ (2016)
-  **Pix2pix**: `Image-to-Image Translation with Conditional Adversarial
   Networks <https://arxiv.org/pdf/1611.07004.pdf>`__ (2016)
-  **DTN**: `Unsupervised Cross-domain Image
   Generation <https://arxiv.org/pdf/1611.02200.pdf>`__ (2016)
-  **SimGAN**: `Learning from Simulated and Unsupervised Images through
   Adversarial Training (2016) <https://arxiv.org/pdf/1612.07828.pdf>`__
   (2016)
-  **PixelDA**: `Unsupervised Pixel–Level Domain Adaptation with
   Generative Adversarial
   Networks <https://arxiv.org/pdf/1612.05424.pdf>`__ (2016)
-  **UNIT**: `Unsupervised Image-to-Image Translation
   Networks <https://arxiv.org/pdf/1703.00848.pdf>`__ (2017)
-  **CycleGAN**: `Unpaired Image-to-Image Translation using
   Cycle-Consistent Adversarial
   Networks <https://arxiv.org/pdf/1703.10593>`__ (2017)
-  **DiscoGAN**: `Learning to Discover Cross-Domain Relations with
   Generative Adversarial
   Networks <https://arxiv.org/pdf/1703.05192.pdf>`__ (2017)
-  **DualGAN**: `DualGAN: Unsupervised Dual Learning for Image-to-Image
   Translation <https://arxiv.org/pdf/1704.02510.pdf>`__ (2017)
-  **SBADA-GAN**: `From source to target and back: symmetric
   bi-directional adaptive GAN <https://arxiv.org/pdf/1705.08824.pdf>`__
   (2017)
-  **DistanceGAN**: `One-Sided Unsupervised Domain
   Mapping <https://arxiv.org/pdf/1706.00826.pdf>`__ (2017)
-  **pix2pixHD**: `High-Resolution Image Synthesis and Semantic
   Manipulation with Conditional
   GANs <https://arxiv.org/pdf/1711.11585.pdf>`__ (2018)
-  **I2I**: `Image to Image Translation for Domain
   Adaptation <https://arxiv.org/pdf/1712.00479.pdf>`__ (2017)
-  **MUNIT**: `Multimodal Unsupervised Image-to-Image
   Translation <https://arxiv.org/abs/1804.04732>`__ (2018)

Multi-source adaptation
'''''''''''''''''''''''

-  **StarGAN**: `StarGAN: Unified Generative Adversarial Networks for
   Multi-Domain Image-to-Image
   Translation <https://arxiv.org/pdf/1711.09020.pdf>`__ (2017)
-  **XGAN**: `XGAN: Unsupervised Image-to-Image Translation for
   Many-to-Many Mappings <https://arxiv.org/pdf/1711.05139.pdf>`__
   (2017)
-  **BicycleGAN** : `Toward Multimodal Image-to-Image
   Translation <https://arxiv.org/pdf/1711.11586.pdf>`__ (2017)
-  `Label Efficient Learning of Transferable Representations across
   Domains and Tasks <https://arxiv.org/pdf/1712.00123.pdf>`__ (2017)
-  **ComboGAN**: `ComboGAN: Unrestrained Scalability for Image Domain
   Translation <https://arxiv.org/pdf/1712.06909.pdf>`__ (2017)
-  **AugCGAN**: `Augmented CycleGAN: Learning Many-to-Many Mappings from
   Unpaired Data <https://arxiv.org/abs/1802.10151>`__ (2018)
-  **RadialGAN**: `RadialGAN: Leveraging multiple datasets to improve
   target-specific predictive models using Generative Adversarial
   Networks <https://arxiv.org/abs/1802.06403>`__ (2018)
-  **MADA**: `Multi-Adversarial Domain
   Adaptation <https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/17067/16644>`__
   (2018)
-  **MDAN**: `Multiple Source Domain Adaptation with Adversarial
   Learning <https://arxiv.org/pdf/1705.09684.pdf>`__ (2018)

Temporal models (videos)
''''''''''''''''''''''''

-  **Model F**: `Unsupervised Domain Adaptation for Face Recognition in
   Unlabeled Videos <https://arxiv.org/pdf/1708.02191.pdf>`__ (2017)
-  **RecycleGAN**: `Recycle-GAN: Unsupervised Video
   Retargeting <https://arxiv.org/pdf/1808.05174.pdf>`__ (2018)
-  **Vid2vid**: `Video-to-Video
   Synthesis <https://arxiv.org/pdf/1808.06601.pdf>`__ (2018)
-  **Temporal Smoothing (TS)**: `Everybody Dance
   Now <https://arxiv.org/pdf/1808.07371.pdf>`__ (2018)

Optimal Transport
^^^^^^^^^^^^^^^^^

-  **OT**: `Optimal Transport for Domain
   Adaptation <https://arxiv.org/pdf/1507.00504.pdf>`__ (2015)
-  `Theoretical Analysis of Domain Adaptation with Optimal
   Transport <https://arxiv.org/pdf/1610.04420.pdf>`__ (2016)
-  **JDOT**: `Joint distribution optimal transportation for domain
   adaptation <https://arxiv.org/pdf/1705.08848.pdf>`__ (2017)
-  **Monge map learning**: `Large Scale Optimal Transport and Mapping
   Estimation <https://arxiv.org/pdf/1711.02283.pdf>`__ (2017)
-  **JCPOT**: `Optimal Transport for Multi-source Domain Adaptation
   under Target Shift <https://arxiv.org/pdf/1803.04899.pdf>`__ (2018)
-  **DeepJDOT**: `DeepJDOT: Deep Joint distribution optimal transport
   for unsupervised domain
   adaptation <https://arxiv.org/pdf/1803.10081.pdf>`__ (2018)

Embedding methods
^^^^^^^^^^^^^^^^^

-  `Unsupervised Domain Adaptation for Zero-Shot
   Learning <https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Kodirov_Unsupervised_Domain_Adaptation_ICCV_2015_paper.pdf>`__
   (2015)
-  **DAassoc** : `Associative Domain
   Adaptation <https://arxiv.org/pdf/1708.00938.pdf>`__ (2017)

Kernel methods
^^^^^^^^^^^^^^

-  **SurK**: `Covariate Shift in Hilbert Space: A Solution via Surrogate
   Kernels <https://pdfs.semanticscholar.org/edb8/be020e228153163428e8b698aef1af4c5cad.pdf>`__
   (2015)
-  **DAN**: `Learning Transferable Features with Deep Adaptation
   Networks <https://arxiv.org/pdf/1502.02791.pdf>`__ (2015)
-  **RTN**: `Unsupervised Domain Adaptation with Residual Transfer
   Networks <https://arxiv.org/pdf/1602.04433.pdf>`__ (2016)
-  **Easy DA**: `A Simple Approach for Unsupervised Domain
   Adaptation <http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899860>`__
   (2016)

Autoencoder approach
^^^^^^^^^^^^^^^^^^^^

-  **MCAE**: `Learning Classifiers from Synthetic Data Using a
   Multichannel Autoencoder <https://arxiv.org/pdf/1503.03163.pdf>`__
   (2015)
-  **SMCAE**: `Learning from Synthetic Data Using a Stacked Multichannel
   Autoencoder <https://arxiv.org/pdf/1509.05463.pdf>`__ (2015)

Subspace Learning
^^^^^^^^^^^^^^^^^

-  **SGF**: `Domain Adaptation for Object Recognition: An Unsupervised
   Approach <https://pdfs.semanticscholar.org/d3ed/bfee56884d2b6d9aa51a6c525f9a05248802.pdf>`__
   (2011)
-  **GFK**: `Geodesic Flow Kernel for Unsupervised Domain
   Adaptation <https://pdfs.semanticscholar.org/0a59/337568cbf74e7371fb543f7ca34bbc2153ac.pdf>`__
   (2012)
-  **SA**: `Unsupervised Visual Domain Adaptation Using Subspace
   Alignment <https://pdfs.semanticscholar.org/51a4/d658c93c5169eef7568d3d1cf53e8e495087.pdf>`__
   (2015)
-  **CORAL**: `Return of Frustratingly Easy Domain
   Adaptation <https://arxiv.org/pdf/1511.05547.pdf>`__ (2015)
-  **Deep CORAL**: `Deep CORAL: Correlation Alignment for Deep Domain
   Adaptation <https://arxiv.org/pdf/1607.01719.pdf>`__ (2016)
-  **ILS**: `Learning an Invariant Hilbert Space for Domain
   Adaptation <https://arxiv.org/pdf/1611.08350.pdf>`__ (2016)
-  **Log D-CORAL**: `Correlation Alignment by Riemannian Metric for
   Domain Adaptation <https://arxiv.org/pdf/1705.08180.pdf>`__ (2017)

Self-Ensembling methods
^^^^^^^^^^^^^^^^^^^^^^^

-  **MT**: `Self-ensembling for domain
   adaptation <https://arxiv.org/pdf/1706.05208.pdf>`__ (2017)

Other
^^^^^

-  `Adapting Visual Category Models to New
   Domains <https://scalable.mpi-inf.mpg.de/files/2013/04/saenko_eccv_2010.pdf>`__
   (2010)
-  **AdaBN**: `Revisiting Batch Normalization for Practical Domain
   Adaptation <https://arxiv.org/pdf/1603.04779.pdf>`__ (2016)

Semi-supervised Domain Adaptation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

All the source points are labelled, but only few target points are.

General methods
^^^^^^^^^^^^^^^

-  **da+lap-sim** : `Semi-Supervised Domain Adaptation with Instance
   Constraints <http://jeffdonahue.com/papers/DAInstanceConstraintsCVPR2013.pdf>`__
   (2013)

Subspace learning
^^^^^^^^^^^^^^^^^

-  **EA++**: `Co-regularization Based Semi-supervised Domain
   Adaptation <https://papers.nips.cc/paper/4009-co-regularization-based-semi-supervised-domain-adaptation.pdf>`__
   (2010)
-  **SDASL**: `Semi-supervised Domain Adaptation with Subspace Learning
   for Visual
   Recognition <https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Yao_Semi-Supervised_Domain_Adaptation_2015_CVPR_paper.pdf>`__
   (2015)

Copulas methods
^^^^^^^^^^^^^^^

-  **NPRV**: `Semi-Supervised Domain Adaptation with Non-Parametric
   Copulas <https://papers.nips.cc/paper/4802-semi-supervised-domain-adaptation-with-non-parametric-copulas.pdf>`__
   (2013)

Few-shot Supervised Domain Adaptation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Only a few target examples are available, but they are labelled

Adversarial methods
^^^^^^^^^^^^^^^^^^^

-  **FADA**: `Few-Shot Adversarial Domain
   Adaptation <https://arxiv.org/pdf/1711.02536.pdf>`__ (2017)
-  **Augmented-Cyc**: `Augmented Cyclic Adversarial Learning for Domain
   Adaptation <https://arxiv.org/pdf/1807.00374.pdf>`__ (2018)

Embedding methods
^^^^^^^^^^^^^^^^^

-  **CCSA**: `Unified Deep Supervised Domain Adaptation and
   Generalization <https://arxiv.org/pdf/1709.10190.pdf>`__ (2017)

Applied Domain Adaptation
~~~~~~~~~~~~~~~~~~~~~~~~~

Domain adaptation applied to other fields

Physics
^^^^^^^

-  `Learning to Pivot with Adversarial
   Networks <http://papers.nips.cc/paper/6699-learning-to-pivot-with-adversarial-networks.pdf>`__
   (2016)
-  `Adversarial Domain Adaptation for Identifying Phase
   Transitions <https://arxiv.org/pdf/1710.08382.pdf>`__ (2017)

-  `Identifying Quantum Phase Transitions with Adversarial Neural
   Networks <https://arxiv.org/abs/1710.08382>`__ (2017)

-  `Automated discovery of characteristic features of phase transitions
   in many-body localization <https://arxiv.org/abs/1806.00419>`__
   (2017)

Audio Processing
^^^^^^^^^^^^^^^^

-  `Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion
   Recognition <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6817520>`__
   (2014)
-  `Adversarial Teacher-Student Learning for Unsupervised Domain
   Adaptation <https://arxiv.org/pdf/1804.00644.pdf>`__ (2018)

Datasets
--------

Image-to-image
~~~~~~~~~~~~~~

-  `MNIST <http://yann.lecun.com/exdb/mnist/>`__ vs
   `MNIST-M <https://drive.google.com/file/d/0B9Z4d7lAwbnTNDdNeFlERWRGNVk/view>`__
   vs `SVHN <http://ufldl.stanford.edu/housenumbers/>`__ vs
   `Synth <https://drive.google.com/file/d/0B9Z4d7lAwbnTSVR1dEFSRUFxOUU/view>`__
   vs
   `USPS <http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps>`__:
   digit images
-  `GTSRB <http://benchmark.ini.rub.de/?section=gtsrb&subsection=news>`__
   vs `Syn Signs <http://graphics.cs.msu.ru/en/node/1337>`__ : traffic
   sign recognition datasets, transfer between real and synthetic signs.
-  `NYU Depth Dataset
   V2 <http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html>`__:
   labeled paired images taken with two different cameras (normal and
   depth)
-  `CelebA <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`__: faces
   of celebrities, offering the possibility to perform gender or hair
   color translation for instance
-  `Office-Caltech
   dataset <https://people.eecs.berkeley.edu/~jhoffman//domainadapt/>`__:
   images of office objects from 10 common categories shared by the
   Office-31 and Caltech-256 datasets. There are in total four domains:
   Amazon, Webcam, DSLR and Caltech.
-  `Cityscapes dataset <https://www.cityscapes-dataset.com/>`__: street
   scene photos (source) and their annoted version (target)
-  `UnityEyes <http://www.cl.cam.ac.uk/research/rainbow/projects/unityeyes/>`__
   vs
   `MPIIGaze <https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild-mpiigaze/>`__:
   simulated vs real gaze images (eyes)
-  `CycleGAN
   datasets <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`__:
   horse2zebra, apple2orange, cezanne2photo, monet2photo, ukiyoe2photo,
   vangogh2photo, summer2winter
-  `pix2pix
   dataset <https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/>`__:
   edges2handbags, edges2shoes, facade, maps
-  `RaFD <http://www.socsci.ru.nl:8180/RaFD2/RaFD?p=main>`__: facial
   images with 8 different emotions (anger, disgust, fear, happiness,
   sadness, surprise, contempt, and neutral). You can transfer a face
   from one emotion to another.
-  `VisDA 2017 classification
   dataset <http://ai.bu.edu/visda-2017/#browse>`__: 12 categories of
   object images in 2 domains: 3D-models and real images.
-  `Office-Home dataset <http://hemanthdv.org/OfficeHome-Dataset/>`__:
   images of objects in 4 domains: art, clipart, product and real-world.

Text-to-text
~~~~~~~~~~~~

-  `Amazon review benchmark
   dataset <https://www.cs.jhu.edu/~mdredze/datasets/sentiment/>`__:
   sentiment analysis for four kinds (domains) of reviews: books, DVDs,
   electronics, kitchen
-  `ECML/PKDD Spam
   Filtering <http://www.ecmlpkdd2006.org/challenge.html#download>`__:
   emails from 3 different inboxes, that can represent the 3 domains.
-  `20 Newsgroup <http://qwone.com/~jason/20Newsgroups/>`__: collection
   of newsgroup documents across 6 top categories and 20 subcategories.
   Subcategories can play the role of the domains, as describe in `this
   article <https://arxiv.org/pdf/1707.01217.pdf>`__.

Results
-------

The results are indicated as the prediction accuracy (in %) in the
target domain after adapting the source to the target. For the moment,
they only correspond to the results given in the original papers, so the
methodology may vary between each paper and these results must be taken
with a grain of salt.

Digits transfer (unsupervised)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

+------+------+------+------+------+------+------+
| Sour | MNIS | Synt | MNIS | SVHN | MNIS | USPS |
| ceTa | TMNI | hSVH | TSVH | MNIS | TUSP | MNIS |
| rget | ST-M | N    | N    | T    | S    | T    |
+======+======+======+======+======+======+======+
| SA   | 56.9 | 86.4 | ?    | 59.3 | ?    | ?    |
|      | 0    | 4    |      | 2    |      |      |
+------+------+------+------+------+------+------+
| DANN | 76.6 | 91.0 | ?    | 73.8 | ?    | ?    |
|      | 6    | 9    |      | 5    |      |      |
+------+------+------+------+------+------+------+
| CoGA | ?    | ?    | ?    | ?    | 91.2 | 89.1 |
| N    |      |      |      |      |      |      |
+------+------+------+------+------+------+------+
| DRCN | ?    | ?    | 40.0 | 81.9 | 91.8 | 73.6 |
|      |      |      | 5    | 7    | 0    | 7    |
+------+------+------+------+------+------+------+
| DSN  | 83.2 | 91.2 | ?    | 82.7 | ?    | ?    |
+------+------+------+------+------+------+------+
| DTN  | ?    | ?    | 90.6 | 79.7 | ?    | ?    |
|      |      |      | 6    | 2    |      |      |
+------+------+------+------+------+------+------+
| Pixe | 98.2 | ?    | ?    | ?    | 95.9 | ?    |
| lDA  |      |      |      |      |      |      |
+------+------+------+------+------+------+------+
| ADDA | ?    | ?    | ?    | 76.0 | 89.4 | 90.1 |
+------+------+------+------+------+------+------+
| UNIT | ?    | ?    | ?    | 90.5 | 95.9 | 93.5 |
|      |      |      |      | 3    | 7    | 8    |
+------+------+------+------+------+------+------+
| GenT | ?    | ?    | ?    | 92.4 | 95.3 | 90.8 |
| oAda |      |      |      |      |      |      |
| pt   |      |      |      |      |      |      |
+------+------+------+------+------+------+------+
| SBAD | 99.4 | ?    | 61.1 | 76.1 | 97.6 | 95.0 |
| A-GA |      |      |      |      |      |      |
| N    |      |      |      |      |      |      |
+------+------+------+------+------+------+------+
| DAas | 89.4 | 91.8 | ?    | 97.6 | ?    | ?    |
| soc  | 7    | 6    |      | 0    |      |      |
+------+------+------+------+------+------+------+
| CyCA | ?    | ?    | ?    | 90.4 | 95.6 | 96.5 |
| DA   |      |      |      |      |      |      |
+------+------+------+------+------+------+------+
| I2I  | ?    | ?    | ?    | 92.1 | 95.1 | 92.2 |
+------+------+------+------+------+------+------+
| DIRT | 98.7 | ?    | 76.5 | 99.4 | ?    | ?    |
| -T   |      |      |      |      |      |      |
+------+------+------+------+------+------+------+
| Deep | 92.4 | ?    | ?    | 96.7 | 95.7 | 96.4 |
| JDOT |      |      |      |      |      |      |
+------+------+------+------+------+------+------+

Challenges
----------

-  `Visual Domain Adaptation Challenge
   (VisDA) <http://ai.bu.edu/visda-2017/>`__
-  `Open AI Retro Contest <https://blog.openai.com/retro-contest/>`__

Libraries
---------

No good library for the moment (as far as I know). If you're interested
in a project of creating a generic transfer learning/domain adaptation
library, please let me know.