Unsupervised Domain Adaptation

477 papers with code • 23 benchmarks • 22 datasets

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


Use these libraries to find Unsupervised Domain Adaptation models and implementations

Most implemented papers

Person Transfer GAN to Bridge Domain Gap for Person Re-Identification

damo-cv/transreid CVPR 2018

Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e. g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network.

Unsupervised Domain Adaptation by Backpropagation

PaddlePaddle/PaddleSpeech 26 Sep 2014

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).

Adversarial Discriminative Domain Adaptation

thuml/Transfer-Learning-Library CVPR 2017

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

Deep CORAL: Correlation Alignment for Deep Domain Adaptation

thuml/Transfer-Learning-Library 6 Jul 2016

CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation.

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

mil-tokyo/MCD_DA CVPR 2018

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.

Domain Adaptive Faster R-CNN for Object Detection in the Wild

yuhuayc/da-faster-rcnn CVPR 2018

The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.

Rescaling Egocentric Vision

epic-kitchens/epic-kitchens-100-annotations 23 Jun 2020

This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

tensorflow/models CVPR 2017

Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.

Domain Separation Networks

tensorflow/models NeurIPS 2016

However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

google-research/adamatch ICLR 2022

We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.