Unsupervised Domain Adaptation Edit Task
Methodology • Domain Adaptation
About
Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
Benchmarks
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Datasets
Greatest papers with code
Domain Separation Networks
NeurIPS 2016
• tensorflow/models
• 
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.
Ranked #1 on
Domain Adaptation
on Synth Digits-to-SVHN
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
CVPR 2017
• tensorflow/models
•
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
FDA: Fourier Domain Adaptation for Semantic Segmentation
CVPR 2020
• albumentations-team/albumentations
• 
We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other.
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
• jindongwang/transferlearning
•
In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating UDA models.
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
• jindongwang/transferlearning
•
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
Ranked #1 on
Domain Adaptation
on Office-Caltech-10
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
• zhaoxin94/awesome-domain-adaptation
•
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
A Survey of Unsupervised Deep Domain Adaptation
• zhaoxin94/awsome-domain-adaptation
•
Deep learning has produced state-of-the-art results for a variety of tasks.
Learning Generalisable Omni-Scale Representations for Person Re-Identification
• KaiyangZhou/deep-person-reid
•
An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.
UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED PERSON RE-IDENTIFICATION
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
CVPR 2019
• thuml/Transfer-Learning-Library
•
Semantic segmentation is a key problem for many computer vision tasks.
IMAGE-TO-IMAGE TRANSLATION SEMANTIC SEGMENTATION UNSUPERVISED DOMAIN ADAPTATION
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
ICCV 2019
• thuml/Transfer-Learning-Library
•
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Ranked #5 on
Domain Adaptation
on ImageCLEF-DA
PARTIAL DOMAIN ADAPTATION TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION