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/a-abedi/euda
공식 코드EUDA: An Efficient Unsupervised Domain Adaptation via Self-Supervised Vision Transformer
https://github.com/a514514772/DISE-Domain-Invariant-Structure-Extraction
공식 코드https://github.com/adrianchifu/FreSaDa
공식 코드FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection
https://github.com/agrija9/deep-unsupervised-domain-adaptation
공식 코드Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition
https://github.com/aiiu-lab/blenda
공식 코드BlenDA: Domain Adaptive Object Detection through diffusion-based blending
https://github.com/aipmlab/domain_adaptation
공식 코드Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification
https://github.com/akhilmathurs/libriadapt
공식 코드Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation
https://github.com/akshaymehra24/LimitsOfUDA
공식 코드Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
https://github.com/alibaba/cluster-contrast
공식 코드https://github.com/alpc91/transda
공식 코드Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
https://github.com/altndrr/clup
공식 코드Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
https://github.com/ambekarsameer96/GLSS
공식 코드https://github.com/ameliajimenez/curriculum-federated-learning
공식 코드Memory-aware curriculum federated learning for breast cancer classification
https://github.com/amirmael/semisupmde
공식 코드Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation
https://github.com/amritabh/conda-gen-text-detection
공식 코드ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
https://github.com/andongdeng/bear
공식 코드https://github.com/andre-sacilotti/transferattn-project-code
공식 코드Transferable-guided Attention Is All You Need for Video Domain Adaptation
https://github.com/andrewcaunes/ia4markings
공식 코드https://github.com/anita-hu/sim2real-lane-detection
공식 코드Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving
https://github.com/anita-hu/simulanes
공식 코드Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving
https://github.com/anthonysicilia/pacbayes-adaptation-uai2022
공식 코드PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners
https://github.com/antoinedemathelin/unsupervised-domain-adaptation-for-star-formation-history
공식 코드Unsupervised Domain Adaptation for Constraining Star Formation Histories
https://github.com/arakotom/mars_domain_adaptation
공식 코드Optimal Transport for Conditional Domain Matching and Label Shift
https://github.com/aroncao49/mopa
공식 코드MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation
https://github.com/asbjrnmunk/mdd-unet
공식 코드https://github.com/astra-vision/densemtl
공식 코드DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning
https://github.com/astra-vision/poda
공식 코드https://github.com/astuti/ILA-DA
공식 코드Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation
https://github.com/automan000/Camera-based-Person-ReID
공식 코드Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization
https://github.com/avaapm/SGADA
공식 코드Self-training Guided Adversarial Domain Adaptation For Thermal Imagery
https://github.com/avaapm/stgada
공식 코드Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery
https://github.com/baishuanghao/prompt-based-distribution-alignment
공식 코드Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
https://github.com/barcaaaa/ftd-plusplus
공식 코드https://github.com/bethgelab/robustness
공식 코드Improving robustness against common corruptions by covariate shift adaptation
https://github.com/bilel-bj/unsupervised-domain-adaptation-gan
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.