https://github.com/yyNoBug/VAE_segmentation
공식 코드Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation
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/yzou2/CBST
공식 코드Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
https://github.com/yzou2/CRST
공식 코드https://github.com/zamanzadeh/DACAD
공식 코드DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
https://github.com/zdou0830/DAFE
공식 코드Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings
https://github.com/zenghy96/reliable-source-approximation
공식 코드https://github.com/zhangtravis/hist-da
공식 코드Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
https://github.com/zhao-ht/sida
공식 코드Domain Adaptation via Maximizing Surrogate Mutual Information
https://github.com/zhaoxin94/awesome-domain-adaptation
공식 코드A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
https://github.com/zhaoxin94/awsome-domain-adaptation
공식 코드https://github.com/zhengxxn/uda-knn
공식 코드Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
https://github.com/zhengzangw/PCS-FUDA
공식 코드Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
https://github.com/zhijin-ge/stm
공식 코드Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
https://github.com/zhu-han/castle
공식 코드Boosting Cross-Domain Speech Recognition with Self-Supervision
https://github.com/zhunzhong07/ECN
공식 코드Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
https://github.com/zhuoxiao-chen/redb-da-3ddet
공식 코드https://github.com/zhuzhu804/bba
공식 코드https://github.com/zhyhan/RDA
공식 코드Towards Accurate and Robust Domain Adaptation under Noisy Environments
https://github.com/zhyhan/transpar
공식 코드Learning Transferable Parameters for Unsupervised Domain Adaptation
https://github.com/zijunli7/condo
공식 코드ConDo: Continual Domain Expansion for Absolute Pose Regression
https://github.com/ziqiaowanggeothe/f-dd
공식 코드On $f$-Divergence Principled Domain Adaptation: An Improved Framework
https://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/zjy526223908/BTDA
공식 코드Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
https://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/zwenyu/colearn-plus
공식 코드Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation
https://github.com/zywang0701/drl-simus
공식 코드Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
https://gitlab.com/dlr-dw/type-inference
공식 코드Cross-Domain Evaluation of a Deep Learning-Based Type Inference System
https://gitlab.com/tringwald/cvp
공식 코드Certainty Volume Prediction for Unsupervised Domain Adaptation
https://gitlab.com/tringwald/ubr2s
공식 코드UBR$^2$S: Uncertainty-Based Resampling and Reweighting Strategy for Unsupervised Domain Adaptation
https://gitlab.com/tringwald/ufal
공식 코드Unsupervised Domain Adaptation by Uncertain Feature Alignment
https://worksheets.codalab.org/worksheets/0x2613c72d4f3f4fbb94e0a32c17ce5fb0
공식 코드https://worksheets.codalab.org/worksheets/0xabfe206d857d40a5baf2a19ece5f2659
공식 코드https://github.com/2020132075/conmae
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.