Awesome Transfer Learning

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
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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.

Note: this list is not actively maintained anymore, but I still accept pull requests, so please don't hesitate if you want to contribute with newer resources

Table of Contents

Tutorials and Blogs


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.


Deep Transfer Learning

Transfer of deep learning models.

Fine-tuning approach

Feature extraction (embedding) approach

Multi-task learning

Policy transfer for RL

Few-shot transfer learning

Meta transfer learning


Medical imaging:


Anomaly Detection

Unsupervised Domain Adaptation

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




Adversarial methods

Learning a latent space

Image-to-Image translation

Multi-source adaptation

Temporal models (videos)

Optimal Transport

Embedding methods

Kernel methods

Autoencoder approach

Subspace Learning

Self-Ensembling methods


Semi-supervised Domain Adaptation

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

General methods

Subspace learning

Copulas methods

Few-shot Supervised Domain Adaptation

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

Adversarial methods

Embedding methods

Applied Domain Adaptation

Domain adaptation applied to other fields


Audio Processing



  • MNIST vs MNIST-M vs SVHN vs Synth vs USPS: digit images
  • GTSRB vs Syn Signs : traffic sign recognition datasets, transfer between real and synthetic signs.
  • NYU Depth Dataset V2: labeled paired images taken with two different cameras (normal and depth)
  • CelebA: faces of celebrities, offering the possibility to perform gender or hair color translation for instance
  • Office-Caltech dataset: 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: street scene photos (source) and their annoted version (target)
  • UnityEyes vs MPIIGaze: simulated vs real gaze images (eyes)
  • CycleGAN datasets: horse2zebra, apple2orange, cezanne2photo, monet2photo, ukiyoe2photo, vangogh2photo, summer2winter
  • pix2pix dataset: edges2handbags, edges2shoes, facade, maps
  • RaFD: 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: 12 categories of object images in 2 domains: 3D-models and real images.
  • Office-Home dataset: images of objects in 4 domains: art, clipart, product and real-world.
  • DukeMTMC-reid and Market-1501: two pedestrian datasets collected at different places. The evaluation metric is based on open-set image retrieval.




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)

SA 56.90 86.44 ? 59.32 ? ?
DANN 76.66 91.09 ? 73.85 ? ?
iDANN 96.67 91.95 36.49 84.50 ? ?
CoGAN ? ? ? ? 91.2 89.1
DRCN ? ? 40.05 81.97 91.80 73.67
DSN 83.2 91.2 ? 82.7 ? ?
DTN ? ? 90.66 79.72 ? ?
PixelDA 98.2 ? ? ? 95.9 ?
ADDA ? ? ? 76.0 89.4 90.1
UNIT ? ? ? 90.53 95.97 93.58
GenToAdapt ? ? ? 92.4 95.3 90.8
SBADA-GAN 99.4 ? 61.1 76.1 97.6 95.0
DAassoc 89.47 91.86 ? 97.60 ? ?
CyCADA ? ? ? 90.4 95.6 96.5
I2I ? ? ? 92.1 95.1 92.2
DIRT-T 98.7 ? 76.5 99.4 ? ?
DeepJDOT 92.4 ? ? 96.7 95.7 96.4
DTA ? ? ? 99.4 99.5 99.1
LSTNet ? ? ? ? 97.61 97.01



  • Domain Adaptation: Salad (Semi-supervised Adaptive Learning Across Domains)


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Transfer Learning
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