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/leekyungmoon/XDED
공식 코드Cross-Domain Ensemble Distillation for Domain Generalization
https://github.com/leilimaster/DmifNet
공식 코드DmifNet:3D Shape Reconstruction Based on Dynamic Multi-Branch Information Fusion
https://github.com/leitong02/clanet
공식 코드CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images
https://github.com/leminhbinh0209/cvpr24-fas
공식 코드https://github.com/leo-chu/semanticloc
공식 코드https://github.com/leo02016/mentorgnn
공식 코드https://github.com/leojklarner/q-savi
공식 코드Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
https://github.com/leokarlin/brad
공식 코드Unsupervised Domain Generalization by Learning a Bridge Across Domains
https://github.com/leolyj/DsCML
공식 코드https://github.com/leolyj/dca-srsfe
공식 코드https://github.com/leolyj/san-saw
공식 코드https://github.com/leon-francis/factual
공식 코드https://github.com/less-and-less-bugs/rdcm
공식 코드Robust Domain Misinformation Detection via Multi-modal Feature Alignment
https://github.com/leuchine/self_play_picard
공식 코드https://github.com/lhaof/asc
공식 코드https://github.com/lhf12278/DRDL
공식 코드Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification
https://github.com/lhoyer/DAFormer
공식 코드https://github.com/lhoyer/hrda
공식 코드HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth
공식 코드https://github.com/lhoyer/mic
공식 코드MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
https://github.com/li-aolong/Word-Matters
공식 코드Word Matters: What Influences Domain Adaptation in Summarization?
https://github.com/li-xirong/ude
공식 코드https://github.com/liamheng/annotation-free-medical-image-enhancement
공식 코드https://github.com/liamheng/non-iid_medical_image_segmentation
공식 코드Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
https://github.com/liangchen527/causeb
공식 코드A Causal Inspired Early-Branching Structure for Domain Generalization
https://github.com/liangchen527/itta
공식 코드https://github.com/liangchen527/lfme
공식 코드LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
https://github.com/liangchen527/ridg
공식 코드https://github.com/liangcici/probes-vln
공식 코드Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration
https://github.com/licongguan/ilm-assl
공식 코드https://github.com/lidar-perception/lidar-cs
공식 코드LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
https://github.com/light-47/ida
공식 코드Intermediate Domain-guided Adaptation for Unsupervised Chorioallantoic Membrane Vessel Segmentation
https://github.com/liguge/PyDSN
공식 코드https://github.com/lijichang/CVPR2021-SSDA
공식 코드Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation
https://github.com/lijin118/3CATN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.