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/2023-MindSpore-1/ms-code-177
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-1/ms-code-201
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-1/ms-code-22
https://github.com/2023-MindSpore-1/ms-code-86
https://github.com/2023-MindSpore-4/Code-5/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-4/Code10/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code11/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-4/Code11/tree/main/ssc_resnet50
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code3/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code8/tree/main/Alexnet
https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet
https://github.com/2024-MindSpore-1/Code5/tree/main/Alexnet
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1
https://github.com/3P2S/arcface
https://github.com/60972823l/SSL-DNLL
Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
https://github.com/AI-Chen/ColorMapGAN
https://github.com/AbhinavAtrishi/semisupervised-multitask-learning
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation
https://github.com/AmirHussein96/Simplified-DSN
https://github.com/Anderies/stochastic-constant-pruninglayers
https://github.com/AngusG/bn-advex-zhang-fixup
https://github.com/Anon6627/KD-UDA
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
https://github.com/AntheaLi/cs224nProject
https://github.com/Armxyz1/Results-on-RFW
Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network
https://github.com/ArnaudBru/Generative-Pseudo-label-Refinement-for-Unsupervised-Domain-Adaptation
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
https://github.com/AyushAniket/ResNET-and-various-normalizations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.