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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/ChenDelong1999/HHForecasting

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A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting

https://github.com/ChengtaoLi/dan

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Distributional Adversarial Networks

https://github.com/ChrisAllenMing/GPA-detection

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Cross-domain Detection via Graph-induced Prototype Alignment

https://github.com/ChrisAllenMing/LtC-MSDA

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Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation

https://github.com/ChristophRaab/nso

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Low-Rank Subspace Override for Unsupervised Domain Adaptation

https://github.com/Chumsy0725/DGWM

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Domain-Guided Weight Modulation for Semi-Supervised Domain Generalization

https://github.com/ChunaraLab/Pop-aware-domain-adaptation

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Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning

https://github.com/Claydon-Wang/OFTTA

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Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition

https://github.com/CuriousAI/mean-teacher

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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/CuthbertCai/SRDA

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Learning Smooth Representation for Unsupervised Domain Adaptation

https://github.com/Cuzyoung/CoDA

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CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning

https://github.com/D1noFuzi/cmd_styletransfer

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In the light of feature distributions: moment matching for Neural Style Transfer

https://github.com/DCSaunders/gender-debias

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Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem

https://github.com/DJEddyking/LF2

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Learning Feature Fusion for Unsupervised Domain Adaptive Person Re-identification

https://github.com/DMIRLAB-Group/SSD

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Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition

https://github.com/DZhaoXd/SND

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Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation

https://github.com/DarioFontanel/OWR-VisualDomains

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On the Challenges of Open World Recognitionunder Shifting Visual Domains

https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG

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Federated Transfer Learning for EEG Signal Classification

https://github.com/Dayan-Guan/DA-VSN

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Domain Adaptive Video Segmentation via Temporal Consistency Regularization

https://github.com/Dayan-Guan/UaDAN

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Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection

https://github.com/DebugML/sqrl

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Do Machine Learning Models Learn Statistical Rules Inferred from Data?

https://github.com/DelTA-Lab-IITK/SFDA

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Domain Impression: A Source Data Free Domain Adaptation Method

https://github.com/DenisPushkin/GOTU-real-valued

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On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions

https://github.com/DequanWang/tent

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Tent: Fully Test-time Adaptation by Entropy Minimization

https://github.com/DianCh/AdaContrast

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Contrastive Test-Time Adaptation

https://github.com/DingDing33/BPS-v1-1

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Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models

https://github.com/Dingkun0817/SDDA

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Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

https://github.com/DirtyHarryLYL/SRDA-ECCV2018

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SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation

https://github.com/Div290/CeeBERT

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CEEBERT: Cross-Domain Inference in Early Exit BERT

https://github.com/Dong-Jie-Chen/RCL

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Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning

https://github.com/DreamtaleCore/PnP-GA

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Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

https://github.com/ETHRuiGong/DLOW

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DLOW: Domain Flow for Adaptation and Generalization

https://github.com/ETHRuiGong/TADA

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TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation

https://github.com/EddCBen/Classifying-Textual-Data-with-pretrained-Vision-Models-through-Transfer-Learning-and-Data-Transforms

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Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data Transformations

https://github.com/EmmaW8/BEAL

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Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

https://github.com/ErikBrorsson/MVUDA

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MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection

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