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
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
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
Applications
Medical imaging:
Robotics
Anomaly Detection
Unsupervised Domain Adaptation
Transfer between a source and a target domain. In unsupervised domain adaptation, only the source domain can have labels.
Theory
General
Multi-source
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
Other
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
Physics
Audio Processing
Datasets
Image-to-image
- 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.
Text-to-text
Other
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)
Source Target |
MNIST MNIST-M |
Synth SVHN |
MNIST SVHN |
SVHN MNIST |
MNIST USPS |
USPS MNIST |
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 |
Challenges
Libraries
- Domain Adaptation: Salad (Semi-supervised Adaptive Learning Across Domains)
Books