Browse SoTA > Methodology > Domain Adaptation > Unsupervised Domain Adaptation

Unsupervised Domain Adaptation

133 papers with code · Methodology
Subtask of Domain Adaptation

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

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Greatest papers with code

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.

UNSUPERVISED DOMAIN ADAPTATION

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.

UNSUPERVISED DOMAIN ADAPTATION

Visual Domain Adaptation with Manifold Embedded Distribution Alignment

19 Jul 2018jindongwang/transferlearning

Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.

TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION

Learning Generalisable Omni-Scale Representations for Person Re-Identification

15 Oct 2019KaiyangZhou/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

A Survey of Unsupervised Deep Domain Adaptation

6 Dec 2018zhaoxin94/awsome-domain-adaptation

Deep learning has produced state-of-the-art results for a variety of tasks.

TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION

Unsupervised Domain Adaptation by Backpropagation

26 Sep 2014fungtion/DANN

Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary).

IMAGE CLASSIFICATION TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

CVPR 2018 mil-tokyo/MCD_DA

To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries.

IMAGE CLASSIFICATION SEMANTIC SEGMENTATION UNSUPERVISED DOMAIN ADAPTATION

Correlation Alignment for Unsupervised Domain Adaptation

6 Dec 2016eridgd/WCT-TF

In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces.

UNSUPERVISED DOMAIN ADAPTATION

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification

CVPR 2019 zhunzhong07/ECN

To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties.

PERSON RE-IDENTIFICATION UNSUPERVISED DOMAIN ADAPTATION

Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification

ICLR 2020 yxgeee/MMT

In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner.

UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED PERSON RE-IDENTIFICATION