Browse > Methodology > Domain Adaptation > Unsupervised Domain Adaptation

Unsupervised Domain Adaptation

64 papers with code · Methodology
Subtask of Domain Adaptation

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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.

UNSUPERVISED DOMAIN ADAPTATION

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

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

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

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 UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Adversarial Discriminative Domain Adaptation

CVPR 2017 erictzeng/adda

Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.

OBJECT CLASSIFICATION UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Learning to cluster in order to transfer across domains and tasks

ICLR 2018 GT-RIPL/L2C

The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.

OMNIGLOT TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION

A DIRT-T Approach to Unsupervised Domain Adaptation

ICLR 2018 domainadaptation/salad

Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.

UNSUPERVISED DOMAIN ADAPTATION