Transfer Learning − Machine Learning's Next Frontier

A Little Review of Domain Adaptation in 2017

A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

A Survey on Transfer Learning

(2009)

Transfer Learning for Reinforcement Learning Domains: A Survey

(2009)

A Survey of transfer learning

(2016)

Domain Adaptation for Visual Applications: A Comprehensive Survey

(2017)

Deep Visual Domain Adaptation: A Survey

(2018)

Do Better ImageNet Models Transfer Better?

(2018)

Using Pre-Training Can Improve Model Robustness and Uncertainty

(2019)

Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

(2020)

Regularizing CNN Transfer Learning with Randomised Regression

(2020)

CNN Features off-the-shelf: an Astounding Baseline for Recognition

(2014)

Taskonomy: Disentangling Task Transfer Learning

(2018)

Learning without forgetting

(2016)

Reinforcement Learning to Train Ms. Pac-Man Using Higher-order Action-relative Inputs

(2013)

Zero-Shot Transfer Learning for Event Extraction

(2017)

Learning a Deep Embedding Model for Zero-Shot Learning

(2017)

Zero-Shot Object Detection

(2018)

LSTD: A Low-Shot Transfer Detector for Object Detection

(2018)

Multidomain Document Layout Understanding using Few Shot Object Detection

(2018)

One-Shot Unsupervised Cross Domain Translation

(2018)

Transfer Learning via Learning to Transfer

(2018)

Deep Convolutional Neural Networks forComputer-Aided Detection: CNN Architectures, Dataset Characteristics and…

(2016)

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

(2017)

Comparison of deep transfer learning strategies for digital pathology

(2018)

A Deep Convolutional Neural Network for Location Recognition and Geometry Based Information

(2018)

Multiresolution Knowledge Distillation for Anomaly Detection

(2020)

A theory of learning from different domains

(2010)

The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings

(2018)

A Theory of Label Propagation for Subpopulation Shift

(2021)

Domain Adaptation with Multiple Sources

(2008)

Algorithms and Theory for Multiple-Source Adaptation

(2018)

Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer

(2019)

Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning

(2020)

Label Efficient Learning of Transferable Representations across Domains and Tasks

(2017)

Theoretical Analysis of Domain Adaptation with Optimal Transport

(2016)

Unsupervised Domain Adaptation for Zero-Shot Learning

(2015)

Adapting Visual Category Models to New Domains

(2010)

AutoDIAL: Automatic Domain Alignment Layers

(2017)

Fully Test-time Adaptation by Entropy Minimization

(2020)

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

(2020)

Improving robustness against common corruptions by covariate shift adaptation

(2020)

Learning to Pivot with Adversarial Networks

(2016)

Identifying Quantum Phase Transitions with Adversarial Neural Networks

(2017)

Automated discovery of characteristic features of phase transitions in many-body localization

(2017)

Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition

(2014)

Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation

(2018)

Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures

(2019)

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.

Amazon review benchmark dataset

sentiment analysis for four kinds (domains) of reviews: books, DVDs, electronics, kitchen

ECML/PKDD Spam Filtering

emails from 3 different inboxes, that can represent the 3 domains.

20 Newsgroup

collection of newsgroup documents across 6 top categories and 20 subcategories. Subcategories can play the role of the domains, as describe in this article.

VisDA 2017 classification dataset

12 categories of object images in 2 domains: 3D-models and real images.

Open AI Retro Contest

Transfer Learning in Action

| GitHub Repo

Transfer Learning for Natural Language Processing

A book that is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models.

Hands-On Transfer Learning with Python

| GitHub Repo

Transfer Learning in Action