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Unsupervised Domain Adaptation
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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/tarun005/USSS_ICCV19
공식 코드https://github.com/teaghan/Cycle_SN
공식 코드Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets
https://github.com/tensorflow/models
공식 코드https://github.com/tfboys-lzz/mpscl
공식 코드https://github.com/thakur-nandan/income
공식 코드https://github.com/theeighthday/co-teaching
공식 코드Co-Teaching for Unsupervised Domain Adaptation and Expansion
https://github.com/thefatbandit/FDA
공식 코드Reproducibility of "FDA: Fourier Domain Adaptation forSemantic Segmentation
https://github.com/theo2021/onda
공식 코드Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions
https://github.com/thudzj/CAT
공식 코드Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
https://github.com/thuml/transfer-caffe
공식 코드Unsupervised Domain Adaptation with Residual Transfer Networks
https://github.com/tim-learn/BA3US
공식 코드A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
https://github.com/tim-learn/Dis-tune
공식 코드DINE: Domain Adaptation from Single and Multiple Black-box Predictors
https://github.com/tim-learn/SHOT
공식 코드https://github.com/tim-learn/SHOT-plus
공식 코드Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
https://github.com/tim-learn/dine
공식 코드DINE: Domain Adaptation from Single and Multiple Black-box Predictors
https://github.com/timtimchang/smile
공식 코드https://github.com/tl-uestc/unimos
공식 코드Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation
https://github.com/tntek/N2DCX
공식 코드Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
https://github.com/tntek/source-free-domain-adaptation
공식 코드https://github.com/tonyckc/uda-bua
공식 코드Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment
https://github.com/tonylekhtman/dilbert
공식 코드https://github.com/toshi2k2/CompnetDA
공식 코드A Bayesian Approach to OOD Robustness in Image Classification
https://github.com/trangvu/guda
공식 코드https://github.com/trangvu/mlm4uda
공식 코드Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
https://github.com/tsun/apa
공식 코드Domain Adaptation with Adversarial Training on Penultimate Activations
https://github.com/tsun/kuda
공식 코드https://github.com/tsun/ssrt
공식 코드Safe Self-Refinement for Transformer-based Domain Adaptation
https://github.com/tue-mps/vfm-uda
공식 코드Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation
https://github.com/uark-cviu/bimal
공식 코드BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
https://github.com/udase-chime2023/jspsych-p.835-listening-test
공식 코드https://github.com/uds-lsv/da-lang-id
공식 코드https://github.com/uiuctml/goat
공식 코드https://github.com/uk-cliplab/representationjsd
공식 코드https://github.com/ukplab/gpl
공식 코드GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
https://github.com/usaito/asymmetric-tri-rec-real
공식 코드Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.