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Unsupervised Domain Adaptation

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평가표: 49
보존된 평가 기록: 393

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/microsoft/ProDA

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Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

https://github.com/microsoft/S2R-DepthNet

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S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation

https://github.com/microsoft/SNR

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Style Normalization and Restitution for Generalizable Person Re-identification

https://github.com/microsoft/UDA

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ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation

https://github.com/miemieyanga/ResiDualGAN-DRDG

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ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation

https://github.com/mil-tokyo/MCD_DA

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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

https://github.com/mims-harvard/raincoat

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Domain Adaptation for Time Series Under Feature and Label Shifts

https://github.com/mingxuangu/few-shot-uda

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Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation

https://github.com/mkirchmeyer/adaptation-imputation

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Unsupervised domain adaptation with non-stochastic missing data

https://github.com/mkirchmeyer/ostar

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Mapping conditional distributions for domain adaptation under generalized target shift

https://github.com/mkmenta/domain_adapt_segm

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Learning to adapt class-specific features across domains for semantic segmentation

https://github.com/mlfoundations/model-soups

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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

https://github.com/mohamedr002/slarda

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Self-supervised Autoregressive Domain Adaptation for Time Series Data

https://github.com/mohamedtev/daca

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Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection

https://github.com/mqinghe/midss

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Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

https://github.com/mqinghe/ust-run

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Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/mrostami1366/SequentialUDA

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Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions

https://github.com/mrzzy2021/sculpturerecon

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3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models

https://github.com/msseibel/accut

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Anatomical Conditioning for Contrastive Unpaired Image-to-Image Translation of Optical Coherence Tomography Images

https://github.com/najaemin92/covi

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Contrastive Vicinal Space for Unsupervised Domain Adaptation

https://github.com/najaemin92/fixbi

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FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation

https://github.com/naver/oasis

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On the Road to Online Adaptation for Semantic Image Segmentation

https://github.com/nazmul-karim170/C-SFDA_Source-Free-Domain-Adaptation

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C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation

https://github.com/neuropoly/domainadaptation

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Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

https://github.com/nthakur20/income

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Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval

https://github.com/oceanypt/CFd

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Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training

https://github.com/oezyurty/cluda

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Contrastive Learning for Unsupervised Domain Adaptation of Time Series

https://github.com/oh-yu/domain-invariant-learning

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Two stages domain invariant representation learners solve the large co-variate shift in unsupervised domain adaptation with two dimensional data domains

https://github.com/onuriel/PermutedAdaIN

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Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification

https://github.com/osamazeeshan/subject-based-domain-adaptation-for-fer

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Subject-Based Domain Adaptation for Facial Expression Recognition

https://github.com/p-lambda/gradual_domain_adaptation

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Understanding Self-Training for Gradual Domain Adaptation

https://github.com/p2irc/UDA4POC

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Unsupervised Domain Adaptation For Plant Organ Counting

https://github.com/pascalschlachter/planalysis

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Analysis of Pseudo-Labeling for Online Source-Free Universal Domain Adaptation

https://github.com/paulalbert31/clover_ssl

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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

https://github.com/pengbohua/angulargap

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Angular Gap: Reducing the Uncertainty of Image Difficulty through Model Calibration

https://github.com/perceivelab/hd2s

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Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction

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