Papers with Code 아카이브

Unsupervised Domain Adaptation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 49
보존된 평가 기록: 393

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/lixinustc/samb-transformeruda

공식 코드

Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation

https://github.com/ljjcoder/PCL

공식 코드

Probabilistic Contrastive Learning for Domain Adaptation

https://github.com/ljjcoder/probabilistic-contrastive-learning

공식 코드

Probabilistic Contrastive Learning for Domain Adaptation

https://github.com/lkpengcs/dcda

공식 코드

Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning

https://github.com/lkpengcs/rvms

공식 코드

Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation

https://github.com/lluckyyh/vt-ada

공식 코드

Vision Transformer-based Adversarial Domain Adaptation

https://github.com/lonelyhope/dada

공식 코드

Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

https://github.com/louey233/toward-mutual-information

공식 코드

Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

https://github.com/loyoyang/decota

공식 코드

Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation

https://github.com/lr94/abas

공식 코드

Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

https://github.com/lslrh/T-SVDNet

공식 코드

T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation

https://github.com/lucasfernando-aes/ms-dial

공식 코드

Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers

https://github.com/lugruber/dre_iter_reg

공식 코드

Overcoming Saturation in Density Ratio Estimation by Iterated Regularization

https://github.com/lukemelas/pixmatch

공식 코드

PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training

https://github.com/luyvlei/BiSMAPs

공식 코드

Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation

https://github.com/lxycopper/plu

공식 코드

Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector

https://github.com/lyndonzheng/Synthetic2Realistic

공식 코드

T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks

https://github.com/lynn-yu/hierdamap

공식 코드

HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors

https://github.com/lzhangbj/d3gu

공식 코드

D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment

https://github.com/mainaksingha01/AD-CLIP

공식 코드

AD-CLIP: Adapting Domains in Prompt Space Using CLIP

https://github.com/malicd/sailor

공식 코드

SAILOR: Scaling Anchors via Insights into Latent Object Representation

https://github.com/maohaos2/MSFDA

공식 코드

On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation

https://github.com/mariodoebler/test-time-adaptation

공식 코드

Introducing Intermediate Domains for Effective Self-Training during Test-Time

https://github.com/masilab/da_ct_muscle_seg

공식 코드

Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training

https://github.com/mattialitrico/guiding-pseudo-labels-with-uncertainty-estimation-for-source-free-unsupervised-domain-adaptation

공식 코드

Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

https://github.com/mattialitrico/guiding-pseudo-labels-with-uncertainty-estimation-for-test-time-adaptation

공식 코드

Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

https://github.com/maxiuw/uada3d

공식 코드

UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps

https://github.com/maxwellyin/mdaqa

공식 코드

Source-Free Domain Adaptation for Question Answering with Masked Self-training

https://github.com/mdppml/FREDA

공식 코드

Privacy Preserving Federated Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation Data

https://github.com/mecarill/2pcnet

공식 코드

2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

https://github.com/medicl-vu/crossmoda2023

공식 코드

Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation

https://github.com/meghbhalerao/pred-guide

공식 코드

Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation

https://github.com/mendicant04/omega

공식 코드

Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment

https://github.com/meshidenn/cai

공식 코드

Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps

https://github.com/mgong2/DA_Infer

공식 코드

Domain Adaptation as a Problem of Inference on Graphical Models

https://github.com/michuanhaohao/transreid-ssl

공식 코드

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.