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Domain Adaptation

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하위 태스크: 13
평가표: 58
보존된 평가 기록: 391

Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.

Further readings:

A Brief Review of Domain Adaptation

<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>

코드

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

https://github.com/RoyalVane/CLAN

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Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

https://github.com/RoyalVane/TAF-Cal

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Test-time Fourier Style Calibration for Domain Generalization

https://github.com/RuiShu/dirt-t

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A DIRT-T Approach to Unsupervised Domain Adaptation

https://github.com/RuiqingDing/SPSSOT

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Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble

https://github.com/Rxannro/SWL-Adapt

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SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition

https://github.com/SCIR-HI/ArtificiallyR2R

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From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery

https://github.com/SCUT-AILab/CPGA

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Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation

https://github.com/SHI-Labs/Unsupervised-Domain-Adaptation-with-Differential-Treatment

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Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation

https://github.com/SRDAMICCAI/SRDA

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Source-Relaxed Domain Adaptation for Image Segmentation

https://github.com/STNLd2/FBPAug

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Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation

https://github.com/SVJayanthi/dmcontrol-generalization-benchmark

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Unified State Representation Learning under Data Augmentation

https://github.com/SamMas-hub/Fat-Segmentation-in-MR-using-domain-adaptation

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Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation

https://github.com/Sapphire-356/TASC

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Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation

https://github.com/SeanLeng1/SCMD

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Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization

https://github.com/SeasonDepth/SeasonDepth

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SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

https://github.com/Sekeh-Lab/FogGuard

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FogGuard: guarding YOLO against fog using perceptual loss

https://github.com/Seung-Hun-Lee/ADAS

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ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation

https://github.com/Seung-Hun-Lee/DRANet

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DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation

https://github.com/ShambhaviCodes/CLIPOT

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Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time Adaptation

https://github.com/ShaohuiLin/SSR

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Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

https://github.com/Shathe/SemiSeg-Contrastive

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Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank

https://github.com/ShawMask/congater_dcase2022t1

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Domain Information Control at Inference Time for Acoustic Scene Classification

https://github.com/Shawey94/WACV2025-FFTAT

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Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation

https://github.com/ShishuaiHu/DCAC

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Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation

https://github.com/SimonThomine/DBFAD

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Distillation-based fabric anomaly detection

https://github.com/SimonThomine/FABLE

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FABLE : Fabric Anomaly Detection Automation Process

https://github.com/SiyuanHuang95/SUG

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SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification

https://github.com/SonyCSLParis/ssl-singer-identity

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Singer Identity Representation Learning using Self-Supervised Techniques

https://github.com/Sophie-Xu/Pose_DomainAdaption

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Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation

https://github.com/StanfordVL/GibsonEnv

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Gibson Env: Real-World Perception for Embodied Agents

https://github.com/Stareru/NeighboringDistributionDivergence

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Contextualized Semantic Distance between Highly Overlapped Texts

https://github.com/SysCV/bdd100k-models

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BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

https://github.com/THU-DA-6D-Pose-Group/Self6D-Diff-Renderer

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Self6D: Self-Supervised Monocular 6D Object Pose Estimation

https://github.com/Tajamul21/Grounded_Teacher

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Context Aware Grounded Teacher for Source Free Object Detection

https://github.com/Tengfei-Wang/DualCameraSR

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Dual-Camera Super-Resolution with Aligned Attention Modules

https://github.com/ThomasWestfechtel/SWG

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Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.