https://github.com/ziqiaowanggeothe/f-dd
공식 코드On $f$-Divergence Principled Domain Adaptation: An Improved Framework
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/ziwei-niu/dg_for_media
공식 코드A Survey on Domain Generalization for Medical Image Analysis
https://github.com/ziyin-dl/global-anchor-method
공식 코드The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
https://github.com/ziyuguo99/image-generation-cot
공식 코드Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPO
https://github.com/ziyujia/mstgcn
공식 코드https://github.com/zju-jiangqi/ciada
공식 코드Computational Imaging for Machine Perception: Transferring Semantic Segmentation beyond Aberrations
https://github.com/zju-jiangqi/qdmr
공식 코드https://github.com/zjunet/GraphAlign
공식 코드https://github.com/zjy526223908/BTDA
공식 코드Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
https://github.com/zkcys001/cfd
공식 코드Calibrated Feature Decomposition for Generalizable Person Re-Identification
https://github.com/zliucr/CrossNER
공식 코드https://github.com/znchen666/hdg
공식 코드PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
https://github.com/zobia111/sdg-alzheimer
공식 코드https://github.com/zou-longkun/gast
공식 코드Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds
https://github.com/zou-longkun/rpd
공식 코드Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers
https://github.com/zou-longkun/tag
공식 코드Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition
https://github.com/zpdu/dai-net
공식 코드Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation
https://github.com/zqyq/Multi-view-People-Detection-in-Large-Scenes-via-View-wise-Contribution-Weighting
공식 코드Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting
https://github.com/zrguo/casp
공식 코드Bridging the Gap for Test-Time Multimodal Sentiment Analysis
https://github.com/zrguo/sumi
공식 코드Smoothing the Shift: Towards Stable Test-Time Adaptation under Complex Multimodal Noises
https://github.com/zshanggu/cdmm
공식 코드Cross-domain Multi-modal Few-shot Object Detection via Rich Text
https://github.com/zwenyu/UniSSDA
공식 코드Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
https://github.com/zwenyu/colearn-plus
공식 코드Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation
https://github.com/zwenyu/lccs
공식 코드Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
https://github.com/zwvews/ladg
공식 코드https://github.com/zxcqlf/robotcar_depthgt_generate
공식 코드Unsupervised Monocular Depth Estimation in Highly Complex Environments
https://github.com/zybeich/DPCore
공식 코드Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation
https://github.com/zybeich/ot-vp
공식 코드OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation
https://github.com/zyezhang/dac
공식 코드Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
https://github.com/zyfone/dpa
공식 코드Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment
https://github.com/zyh-uaiaaaa/generalizable-fer
공식 코드https://github.com/zywang0701/drl-simus
공식 코드Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
https://github.com/zzwdx/ARITH
공식 코드Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization
https://github.com/zzwdx/medic
공식 코드Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization
https://github.com/zzweng/dapa_release
공식 코드Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery
https://github.com/zzxslp/Bilingual-VLN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.