https://github.com/poppinace/TAISL
공식 코드When Unsupervised Domain Adaptation Meets Tensor Representations
Papers with Code 아카이브
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/postBG/DTA.pytorch
공식 코드Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
https://github.com/prasannab29/pacda
공식 코드Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation
https://github.com/primeqa/primeqa
공식 코드UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
https://github.com/pwsong-ustc/ida
공식 코드Intermediate Domain-guided Adaptation for Unsupervised Chorioallantoic Membrane Vessel Segmentation
https://github.com/qi5lei/dsaf
공식 코드https://github.com/qianyuzqy/CAMix
공식 코드Context-Aware Mixup for Domain Adaptive Semantic Segmentation
https://github.com/qianyuzqy/RCCR
공식 코드Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
https://github.com/qingjie99/MetFA
공식 코드Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment
https://github.com/qiurui4shu/PIG
공식 코드https://github.com/rainjamesy/dida
공식 코드Dual-level Interaction for Domain Adaptive Semantic Segmentation
https://github.com/ram95d/ovose
공식 코드OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
https://github.com/ramiMMhamza/Fed-Protoid
공식 코드Privacy-Preserving Adaptive Re-Identification without Image Transfer
https://github.com/ramimmhamza/s2p
공식 코드Source-Guided Similarity Preservation for Online Person Re-Identification
https://github.com/reddyav1/unite
공식 코드Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training
https://github.com/rese1f/PoseDA
공식 코드https://github.com/rflamary/JDOT
공식 코드Joint Distribution Optimal Transportation for Domain Adaptation
https://github.com/ricvolpi/adversarial-feature-augmentation
공식 코드Adversarial Feature Augmentation for Unsupervised Domain Adaptation
https://github.com/ricvolpi/adversarial_feature_augmentation
공식 코드Adversarial Feature Augmentation for Unsupervised Domain Adaptation
https://github.com/rkobler/TSMNet
공식 코드SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
https://github.com/robingerster7/ossa
공식 코드https://github.com/rosanom/habitat-domain-adaptation
공식 코드On Embodied Visual Navigation in Real Environments Through Habitat
https://github.com/rostami-m/ldacid
공식 코드Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay
https://github.com/roysubhankar/dwt-domain-adaptation
공식 코드Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss
https://github.com/rtavenar/MatchAndDeform
공식 코드Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
https://github.com/rtmaww/x-piece
공식 코드Searching for Optimal Subword Tokenization in Cross-domain NER
https://github.com/ruc-aimc-lab/cdcl
공식 코드Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
https://github.com/rucv/MMCD_DA
공식 코드Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/rudrakshkapil/shadowsense
공식 코드https://github.com/ruixxxx/sfuda
공식 코드Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data
https://github.com/rusu4943/3d-sfuda
공식 코드Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images
https://github.com/rvegaml/da_linear
공식 코드https://github.com/ryan315/single2multi-da
공식 코드SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images
https://github.com/ryersonmultimedialab/emotionrecognitionuda
공식 코드https://github.com/salesforce/augtriever
공식 코드AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
https://github.com/saltoricristiano/SF-UDA-3DV
공식 코드SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.