Browse SoTA > Methodology > Domain Adaptation > Unsupervised Domain Adaptation

Unsupervised Domain Adaptation

182 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

Benchmarks

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.

UNSUPERVISED DOMAIN ADAPTATION

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

Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning

25 Mar 2019jindongwang/transferlearning

In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating UDA models.

TRANSFER LEARNING 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 Review of Single-Source Deep Unsupervised Visual Domain Adaptation

1 Sep 2020zhaoxin94/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.

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

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