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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/chomeyama/DualCycleGAN

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Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs

https://github.com/chongqingnosubway/prompt-ot

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Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model Adaptation

https://github.com/chris210634/mudg

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Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap

https://github.com/chris210634/pace-domain-adaptation

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Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling

https://github.com/chris210634/word_soups

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Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning

https://github.com/chrisallenming/cross_category_video_highlight

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Cross-category Video Highlight Detection via Set-based Learning

https://github.com/chrisantuseze/active-self-pretraining

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A3: Active Adversarial Alignment for Source-Free Domain Adaptation

https://github.com/chrishokamp/constrained_decoding

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Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

https://github.com/christianabbet/sra

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Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection

https://github.com/chriswhitmire/alfalfa-yield-prediction

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Comparing Machine Learning Techniques for Alfalfa Biomass Yield Prediction

https://github.com/chu0802/sla

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Semi-Supervised Domain Adaptation with Source Label Adaptation

https://github.com/chumsy0725/fbc-sa

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Towards Generalizing to Unseen Domains with Few Labels

https://github.com/chumsy0725/spsd-vit

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Generalizing to Unseen Domains in Diabetic Retinopathy Classification

https://github.com/chunmeifeng/difftpt

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Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation

https://github.com/cianeastwood/bufr

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Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration

https://github.com/cianeastwood/qrm

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Probable Domain Generalization via Quantile Risk Minimization

https://github.com/cja5553/LLMs_in_perioperative_care

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The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes

https://github.com/ckarouzos/slp_daptmlm

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UDALM: Unsupervised Domain Adaptation through Language Modeling

https://github.com/clbonet/Flowing_Datasets_with_WoW_Gradient_Flows

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Flowing Datasets with Wasserstein over Wasserstein Gradient Flows

https://github.com/clbonet/Sliced-Wasserstein_on_Symmetric_Positive_Definite_Matrices

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Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

https://github.com/clbonet/spdsw

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Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

https://github.com/clement-bonnet/lpn

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Searching Latent Program Spaces

https://github.com/cleverly-ai/multilingual-email-zoning

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Multilingual Email Zoning

https://github.com/cliffbb/uda-nas

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Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping

https://github.com/clovaai/CutMix-PyTorch

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CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

https://github.com/cmcmaster1/tabularlm4ehr

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Adapting Pretrained Language Models for Solving Tabular Prediction Problems in the Electronic Health Record

https://github.com/cmhungsteve/SSTDA

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Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation

https://github.com/cmhungsteve/TA3N

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Temporal Attentive Alignment for Video Domain Adaptation

https://github.com/cmry/amica

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Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity

https://github.com/cnewbie007/DADG

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Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation

https://github.com/cnjianliu/diff9d

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

https://github.com/cobypenso/unsupervised-target-domain-calibration

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Calibration of Network Confidence for Unsupervised Domain Adaptation Using Estimated Accuracy

https://github.com/codezakh/seltda

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Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!

https://github.com/collin-burns/feature-alignment

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Limitations of Post-Hoc Feature Alignment for Robustness

https://github.com/compai-lab/2024-miccai-adsmi-ambekar

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Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations

https://github.com/compstat-lmu/paper_2019_variationalResampleDistributionShift

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Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift

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