https://github.com/xiaowu0162/low-resource-kpgen
공식 코드Representation Learning for Resource-Constrained Keyphrase Generation
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/xiaoyao3302/pcfea
공식 코드https://github.com/xiaoyinliu0714/WMDD
공식 코드https://github.com/xiarho/cmda
공식 코드CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation
https://github.com/xiarho/madm
공식 코드Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation
https://github.com/xiexing0916/ARRA
공식 코드https://github.com/xihechn/QCORAL
공식 코드Quantum correlation alignment for unsupervised domain adaptation
https://github.com/xihechn/QSA
공식 코드https://github.com/xinario/awesome-gan-for-medical-imaging
공식 코드https://github.com/xingchenzhao/DANNCE
공식 코드Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve
https://github.com/xingchenzhao/MixDANN
공식 코드Robust White Matter Hyperintensity Segmentation on Unseen Domain
https://github.com/xingyizhou/3DKeypoints-DA
공식 코드Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
https://github.com/xingyu-group/3d-density-resampling-sdg
공식 코드Domain Generalization of 3D Object Detection by Density-Resampling
https://github.com/xinwu98/sc-wls
공식 코드SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
https://github.com/xinyu-laura/mmuda
공식 코드https://github.com/xjtu-xgu/arpm
공식 코드Adversarial Reweighting with $α$-Power Maximization for Domain Adaptation
https://github.com/xjtu-xgu/kpg-rl
공식 코드https://github.com/xjtushujun/slem-theory
공식 코드Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/xmed-lab/GenericSSL
공식 코드Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
https://github.com/xmed-lab/GraphEcho
공식 코드GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmed-lab/cpr
공식 코드Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
https://github.com/xmed-lab/dfg
공식 코드https://github.com/xmed-lab/triplee-dg
공식 코드https://github.com/xpitfire/iwa
공식 코드Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
https://github.com/xpq-tech/keti
공식 코드Identifying Knowledge Editing Types in Large Language Models
https://github.com/xq141839/nusegdg
공식 코드https://github.com/xuanrui-work/deeplabelalignment
공식 코드https://github.com/xudonmao/RegCGAN
공식 코드Unpaired Multi-Domain Image Generation via Regularized Conditional GANs
https://github.com/xudonmao/VMT
공식 코드https://github.com/xuquanfeng/galaxy_vae
공식 코드https://github.com/xuxw98/backtoreality
공식 코드Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
https://github.com/xuyu0010/atcon
공식 코드Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
https://github.com/xuyu0010/awesome-video-domain-adaptation
공식 코드Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
https://github.com/xuzhez/mapseg
공식 코드https://github.com/xwshen51/drig
공식 코드Causality-oriented robustness: exploiting general noise interventions
https://github.com/xxgege/StableNet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.