https://github.com/Angie-Liu/labelshift
Regularized Learning for Domain Adaptation under Label Shifts
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/AngusG/bn-advex-zhang-fixup
https://github.com/Aniket7/Transfer-Learning
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/Aniket7/VGGNet-CNN-architecture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Anon6627/KD-UDA
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
https://github.com/AntheaLi/cs224nProject
https://github.com/Anuranjan-Kumar/kaggle_cats-vs-dogs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ArifRaihan/Deeper-CNN-Architectures-for-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ArmanAfrasiyabi/associative-alignment-fs
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/ArsenalCheng/Meta-Adapter
https://github.com/Asthestarsfalll/ConvNeXt-MegEngine
https://github.com/Asthestarsfalll/IBNNet-MegEngine
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/Asthestarsfalll/MAE-MegEngine
https://github.com/Aurora11111/speaker-recognition-pytorch
https://github.com/Aurora11111/voiceprint
https://github.com/AveryGriffin/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AwaleSajil/SuperGlue_Demo_Modified1
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/AyushAniket/ResNET-and-various-normalizations
https://github.com/BBillot/SynthSR
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
https://github.com/BCHoagland/DARLA
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
https://github.com/BIG-CHENG/FaceRec
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/VOLO
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt
https://github.com/BUPT-PRIV/MAE-priv
https://github.com/Backdrop9019/adda_pytorch-pseudo-mixup-
https://github.com/Backdrop9019/pytorch_adda_mixup
https://github.com/Baichenjia/Resnet
https://github.com/BaiqiangGit/15minCode
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BalrajAshwath/Adversarial-Domain_Adaptation_Paper_Implementations
https://github.com/Bao-Jiarong/ResNet
https://github.com/BathVisArtData/PeopleArt
https://github.com/BeanGreen247/Python-AI-Arts
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BebDong/MXNetSeg
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.