Papers with Code 아카이브
Unsupervised Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/xcmyz/Face-Binary-Classification
https://github.com/xcmyz/FaceDetection
https://github.com/xcmyz/Face_Binary_Classfication
https://github.com/xhan77/AdaptaBERT
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
https://github.com/xhlulu/arxiv-assistant
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xinge008/SCDA
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xkazm/asfa
Casting a BAIT for Offline and Online Source-free Domain Adaptation
https://github.com/xmed-lab/cardiacnet
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmed-lab/dfg
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
https://github.com/xmed-lab/m3-uda
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xuanyuzhou98/SqueezeSegV2
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/yahoo/open_nsfw
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/gaze-estimation
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
https://github.com/yangboz/TransferLearning4Dentist
https://github.com/yangorwell/NGPlus
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yashclone999/ResNet_MODEL
https://github.com/ycao5602/SAL
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yiyang-gt/feat2vec
https://github.com/yizhe-ang/AdaMatch-PyTorch
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/yjhong89/Domain-Adaptation
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
https://github.com/yoonkicho/bau
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/yoonkicho/pplr
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/yoshitomo-matsubara/torchdistill
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.