Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 58
- 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/kyle-dorman/bayesian-neural-network-blogpost
https://github.com/kynkaat/improved-precision-and-recall-metric
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lRomul/argus-freesound
https://github.com/lab-midas/med_segmentation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lambert-x/prolab
https://github.com/lancerane/Adversarial-domain-adaptation
https://github.com/lastmile-ai/aiconfig
https://github.com/lavoiems/Cats-UDT
https://github.com/lavoiems/SADt
https://github.com/lavoiems/SPUDT
https://github.com/layumi/DG-Net
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/layumi/Person_reID_baseline_pytorch
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/lcc157/code
https://github.com/leanderme/ConvNeXt-Tensorflow
https://github.com/leaplabthu/efficienttrain
https://github.com/leeBooMla/ICSR
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/leebebeto/pytorch-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leeboomla/calr
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/leehomyc/mixup_pytorch
https://github.com/leejoonhun/daf
Domain Adaptation for Time Series Forecasting via Attention Sharing
https://github.com/leelxh/Adaptive-Texture-Filtering-for-Single-Domain-Generalized-Segmentation
Adaptive Texture Filtering for Single-Domain Generalized Segmentation
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/leemsaebom/attention-guided-cam-visual-explanations-of-vision-transformer-guided-by-self-attention
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/leenaali1114/Hierarchical-Image-Clustering---Unsupervised-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leffff/FlowModels
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/leffff/flow-matching-experiments
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/leob03/E2E_Object_Pose_Estimator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/caformer
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/convnext
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/volo
https://github.com/leongatys/DeepTextures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leongatys/PytorchNeuralStyleTransfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
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