https://github.com/user0407/CLUDA
공식 코드CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
Papers with Code 아카이브
Domain Adaptation
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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/userb2020/s3d
공식 코드Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation
https://github.com/usr922/fst
공식 코드Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
https://github.com/ut-amrl/coda-devkit
공식 코드Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
https://github.com/ut-amrl/coda-models
공식 코드Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
https://github.com/ut-austin-data-science-group/mini-batch-ot
공식 코드https://github.com/ut-vision/FgSty-CPL
공식 코드https://github.com/ut-vision/rot-mvgaze
공식 코드Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation
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/utahnlp/marginal_srl_with_semlink
공식 코드Learning Semantic Role Labeling from Compatible Label Sequences
https://github.com/utcsilab/fewmax
공식 코드Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning
https://github.com/utokyo-fieldphenomics-lab/doda
공식 코드DODA: Diffusion for Object-detection Domain Adaptation in Agriculture
https://github.com/uw-exp/globem
공식 코드GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
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/vagver/3dgazenet
공식 코드3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
https://github.com/vagver/dense3deyes
공식 코드3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
https://github.com/vaibhav-khamankar/fusestyle
공식 코드https://github.com/val-iisc/MixupDA
공식 코드Balancing Discriminability and Transferability for Source-Free Domain Adaptation
https://github.com/val-iisc/SFDA-Seg
공식 코드Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
https://github.com/val-iisc/VL2V-ADiP
공식 코드Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
https://github.com/val-iisc/dajat
공식 코드Efficient and Effective Augmentation Strategy for Adversarial Training
https://github.com/val-iisc/dart
공식 코드DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
https://github.com/val-iisc/inheritune
공식 코드https://github.com/val-iisc/s3vaada
공식 코드S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
https://github.com/val-iisc/sdat
공식 코드https://github.com/val-iisc/stickerda
공식 코드Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation
https://github.com/val-iisc/usfda
공식 코드https://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/ConfidNet
공식 코드https://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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.