Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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:
<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/gnobitab/RectifiedFlow
공식 코드Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/goodbai-nlp/amr-domainadaptation
공식 코드https://github.com/google-research/adamatch
공식 코드AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/google-research/augmix
공식 코드AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/COSTAR
공식 코드COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
https://github.com/google-research/google-research/tree/master/domain_conditional_predictors
공식 코드https://github.com/google-research/google-research/tree/master/muNet
공식 코드A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
https://github.com/google-research/google-research/tree/master/talk_about_random_splits
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/adversarialtraining
공식 코드https://github.com/google-research/vision_transformer
공식 코드An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/google/speaker-id/tree/master/lingvo
공식 코드https://github.com/goose315/delta
공식 코드DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
https://github.com/gordon-guojun-zhang/transferability-neurips2021
공식 코드Quantifying and Improving Transferability in Domain Generalization
https://github.com/gorilla-lab-scut/mast
공식 코드Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
https://github.com/gorilla-lab-scut/qs3
공식 코드Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
https://github.com/gorilla-lab-scut/tribe
공식 코드Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization
https://github.com/grammatical/neural-naacl2018
공식 코드Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task
https://github.com/gramuah/semnav
공식 코드SEMNAV: A Semantic Segmentation-Driven Approach to Visual Semantic Navigation
https://github.com/graph-com/pair-align
공식 코드https://github.com/graph-com/strurw
공식 코드https://github.com/gt-salt/guided-adversarial-augmentation
공식 코드https://github.com/gtzheng/advst
공식 코드AdvST: Revisiting Data Augmentations for Single Domain Generalization
https://github.com/guliisgreat/vdpg
공식 코드Adapting to Distribution Shift by Visual Domain Prompt Generation
https://github.com/gungui98/echo-free
공식 코드https://github.com/guoweiyu/spgesture
공식 코드https://github.com/guoxuxu/soft-prompt-transfer
공식 코드Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation
https://github.com/guoyihonggyh/Off-Dynamics-Reinforcement-Learning-via-Domain-Adaptation-and-Reward-Augmented-Imitation
공식 코드Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation
https://github.com/gustavovargashakim/ncttt
공식 코드https://github.com/gustavovargashakim/tttflow
공식 코드TTTFlow: Unsupervised Test-Time Training with Normalizing Flow
https://github.com/guxiao0822/lt-ds
공식 코드Tackling Long-Tailed Category Distribution Under Domain Shifts
https://github.com/gxinuu/pcsr
공식 코드Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation
https://github.com/gzaraunitn/autolabel
공식 코드AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
https://github.com/gzaraunitn/coloseo
공식 코드Simplifying Open-Set Video Domain Adaptation with Contrastive Learning
https://github.com/gzoumpourlis/ensemble-mi
공식 코드Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.