Unsupervised Domain Adaptation
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Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deepskies/domainadaptivemveforlensmodeling
공식 코드https://github.com/deepskies/gnn_domainadapt
공식 코드https://github.com/deeptibhegde/attentiveprototypesfuda
공식 코드Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection
https://github.com/deweihu/adaptdiff
공식 코드https://github.com/div290/dadee
공식 코드https://github.com/djidje/D-MMD
공식 코드Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification
https://github.com/dliu5812/PDAM
공식 코드https://github.com/dliu5812/ddf
공식 코드Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
https://github.com/donglao/PCEDA
공식 코드https://github.com/dongliangchang/Mutual-to-Separate
공식 코드Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
https://github.com/dotrannhattuong/ECB
공식 코드Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
https://github.com/dr-costas/undaw
공식 코드https://github.com/dritter-bht/synthnet-transfer-learning
공식 코드https://github.com/dslrproject/dslr
공식 코드DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder
https://github.com/duojun-huang/diana-cvpr2023
공식 코드Divide and Adapt: Active Domain Adaptation via Customized Learning
https://github.com/dvlab-research/decouplenet
공식 코드DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification
공식 코드Adaptive Exploration for Unsupervised Person Re-Identification
https://github.com/eai-lab/transpl
공식 코드https://github.com/easezyc/deep-transfer-learning
공식 코드https://github.com/ebennequin/meta-domain-shift
공식 코드Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
https://github.com/eddardd/tep-domain-adaptation
공식 코드Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process
https://github.com/ehsanei/lar
공식 코드https://github.com/elchiou/beyond-deterministic-translation-for-uda
공식 코드Beyond Deterministic Translation for Unsupervised Domain Adaptation
https://github.com/elin24/p2rloss
공식 코드Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting
https://github.com/emadeldeen24/adatime
공식 코드ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
https://github.com/emadeldeen24/cotmix
공식 코드Contrastive Domain Adaptation for Time-Series via Temporal Mixup
https://github.com/embar111/pgpc
공식 코드Pseudolabel guided pixels contrast for domain adaptive semantic segmentation
https://github.com/emory-irlab/duqgen
공식 코드https://github.com/enesdoruk/TransAdapter
공식 코드TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation
https://github.com/enesdoruk/damamba
공식 코드DA-Mamba: Domain Adaptive Hybrid Mamba-Transformer Based One-Stage Object Detection
https://github.com/enesdoruk/davimnet
공식 코드DA-Mamba: Domain Adaptive Hybrid Mamba-Transformer Based One-Stage Object Detection
https://github.com/engrjavediqbal/wan
공식 코드Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery
https://github.com/envision-research/generalizable-bev
공식 코드Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
https://github.com/epfl-imos/cat
공식 코드Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
https://github.com/epic-kitchens/epic-kitchens-100-annotations
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.