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/SifatMd/Research-Papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SikaStar/IDM
https://github.com/Simon4Yan/Learning-via-Translation
https://github.com/Skye-Jiang/SuperPoint-Acceleration
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/SnailDev/github-hot-hub
https://github.com/Snarci/YOLO-Para
https://github.com/Solacex/CCM
https://github.com/Solacex/Domain-Consensus-Clustering
https://github.com/SoonminHwang/rgbt-ped-detection
https://github.com/SparkZu/RadioLLM
https://github.com/Spoorthy-gunda/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SrinjaySarkar/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Sshanu/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/StanfordVL/GibsonSim2RealChallenge
https://github.com/Sudhir11292rt/myBDL
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/Suhee05/Text-Independent-Speaker-Verification
https://github.com/SunDoge/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SupratimH/neural-style-transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SupreethRao99/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/SyogoShibuya/Chainer-Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/Sypherd/lm-evaluation-harness
https://github.com/TACJu/TransFG
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/TRI-ML/vidar
Depth Field Networks for Generalizable Multi-view Scene Representation
https://github.com/Taoudi/DataAugmentation
https://github.com/Taoudi/ImbalancedData
https://github.com/Taoudi/LotteryTicketHypothesis
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/TencentYoutuResearch/PersonReID-ACT
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
https://github.com/Terminator8758/CAP-master
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/ThanasisMattas/smartflow
https://github.com/Thanusan19/Vision_Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/The-AI-Summer/self_attention
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/TheTensorDude/vision_transformer_tf
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Thehunk1206/Covid-19-chest-X-ray
https://github.com/Thehunk1206/Covid-19-covidcnn
https://github.com/Thomacdebabo/KP2Dtiny
SuperPoint: Self-Supervised Interest Point Detection and Description
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.