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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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Datasets

Latest papers without code

Unsupervised domain adaptation via double classifiers based on high confidence pseudo label

11 May 2021

In addition to aligning the global distribution, the real domain adaptation should also align the meso distribution and the micro distribution.

TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION

Aggregating From Multiple Target-Shifted Sources

9 May 2021

Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain.

UNSUPERVISED DOMAIN ADAPTATION

More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation

7 May 2021

Feature alignment between domains is one of the mainstream methods for Unsupervised Domain Adaptation (UDA) semantic segmentation.

SEMANTIC SEGMENTATION UNSUPERVISED DOMAIN ADAPTATION

Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation

5 May 2021

To avoid the costly annotation of training data for unseen domains, unsupervised domain adaptation (UDA) attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain.

SEMANTIC SEGMENTATION TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION

Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

5 May 2021

To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss.

UNSUPERVISED DOMAIN ADAPTATION

Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks

28 Apr 2021

This network can achieve source to target domain matching by capturing semantic information at the feature level and producing images for unsupervised domain adaptation from both the source and the target domains.

OBJECT CLASSIFICATION UNSUPERVISED DOMAIN ADAPTATION

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery

28 Apr 2021

Modern deep neural networks (DNNs) achieve highly accurate results for many recognition tasks on overhead (e. g., satellite) imagery.

DATA AUGMENTATION UNSUPERVISED DOMAIN ADAPTATION

Unsupervised Multi-Source Domain Adaptation for Person Re-Identification

27 Apr 2021

Although achieving great success, most of them only use limited data from a single-source domain for model pre-training, making the rich labeled data insufficiently exploited.

PERSON RE-IDENTIFICATION RECTIFICATION UNSUPERVISED DOMAIN ADAPTATION

STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains

23 Apr 2021

We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty.

UNSUPERVISED DOMAIN ADAPTATION

Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval

18 Apr 2021

In this paper, we propose a new domain adaptation method called $\textit{back-training}$, a superior alternative to self-training.

PASSAGE RETRIEVAL QUESTION GENERATION UNSUPERVISED DOMAIN ADAPTATION