https://github.com/usaito/ijcai2022-adversarial-mf
공식 코드Towards Resolving Propensity Contradiction in Offline Recommender Learning
Papers with Code 아카이브
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/user0407/CLUDA
공식 코드CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/usr922/fst
공식 코드Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
https://github.com/uta-smile/ASSUDA
공식 코드Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation
https://github.com/uta-smile/TVT
공식 코드TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation
https://github.com/uzh-rpg/ess
공식 코드ESS: Learning Event-based Semantic Segmentation from Still Images
https://github.com/uzh-rpg/rpg_ev-transfer
공식 코드Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
https://github.com/val-iisc/SFDA-Seg
공식 코드Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
https://github.com/val-iisc/s3vaada
공식 코드S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
https://github.com/val-iisc/stickerda
공식 코드Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation
https://github.com/valentinkoch/svdna
공식 코드Noise transfer for unsupervised domain adaptation of retinal OCT images
https://github.com/valeoai/ADVENT
공식 코드ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/DADA
공식 코드DADA: Depth-aware Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/MTAF
공식 코드Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/MuHDi
공식 코드Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/saluda
공식 코드SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
https://github.com/valeoai/ttyd
공식 코드Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation
https://github.com/valeoai/xmuda
공식 코드xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
https://github.com/valeriopaolicelli/AdAGeo
공식 코드Adaptive-Attentive Geolocalization from few queries: a hybrid approach
https://github.com/valeriopaolicelli/adageo-WACV2021
공식 코드Adaptive-Attentive Geolocalization from few queries: a hybrid approach
https://github.com/vcl-iisc/data-free-defense-at-test-time
공식 코드https://github.com/vcl-iisc/improved-data-free-test-time-adversarial-defense
공식 코드https://github.com/velor2012/tdo-dif
공식 코드Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion
https://github.com/venkatesh-saligrama/pac
공식 코드Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency
https://github.com/vibashan/irg-sfda
공식 코드Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
https://github.com/vibashan/memxformer-online-da
공식 코드https://github.com/vibashan/online-od
공식 코드https://github.com/vibashan/tt-sfuda
공식 코드Target and Task specific Source-Free Domain Adaptive Image Segmentation
https://github.com/viethoang1512/pga
공식 코드Enhancing Domain Adaptation through Prompt Gradient Alignment
https://github.com/vikolss/DACS
공식 코드https://github.com/vilab-ucsd/udabench_eccv2024
공식 코드https://github.com/virajprabhu/SENTRY
공식 코드SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
https://github.com/vision4robotics/udat
공식 코드Unsupervised Domain Adaptation for Nighttime Aerial Tracking
https://github.com/visionflow/srouda_main
공식 코드SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation
https://github.com/visionlearninggroup/snd
공식 코드Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
https://github.com/visualizing-transferred-knowledge/vsualizing-transferred-knowledge
공식 코드Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.