Papers with Code 아카이브

Domain Adaptation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 13
평가표: 58
보존된 평가 기록: 391

Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.

Further readings:

A Brief Review of Domain Adaptation

<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Xinhui-99/UFDA

공식 코드

UFDA: Universal Federated Domain Adaptation with Practical Assumptions

https://github.com/XuMengyaAmy/CIDACaptioning

공식 코드

Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation

https://github.com/XuanhaoLiu/mixEEG

공식 코드

mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup

https://github.com/Xuxiusheng/BatStyler

공식 코드

BatStyler: Advancing Multi-category Style Generation for Source-free Domain Generalization

https://github.com/YBZh/Label-Propagation-with-Augmented-Anchors

공식 코드

Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation

https://github.com/YBZh/MultiClassDA

공식 코드

Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice

https://github.com/YXSong000/UAD

공식 코드

Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation

https://github.com/YYinn/SFDA-DDFP

공식 코드

DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation

https://github.com/Yale-LILY/dart

공식 코드

DART: Open-Domain Structured Data Record to Text Generation

https://github.com/YanchaoYang/FDA

공식 코드

FDA: Fourier Domain Adaptation for Semantic Segmentation

https://github.com/YangZhang4065/AdaptationSeg

공식 코드

Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

https://github.com/Yangsenqiao/ULDA

공식 코드

Unified Language-driven Zero-shot Domain Adaptation

https://github.com/YanzuoLu/MLNet

공식 코드

MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation

https://github.com/Yiyang98/BFUDA

공식 코드

Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation

https://github.com/YoushanZhang/Transfer-Learning

공식 코드

Deep Least Squares Alignment for Unsupervised Domain Adaptation

https://github.com/YoushanZhang/Transfer-Learning/tree/main/Code/Deep/PRPL

공식 코드

Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation

https://github.com/YufanHe/self-domain-adapted-network

공식 코드

Self domain adapted network

https://github.com/YugeTen/fish

공식 코드

Gradient Matching for Domain Generalization

https://github.com/Yuliang-Liu/VimTS

공식 코드

VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization

https://github.com/Yuto-Matsunaga/Prompt_Tuning_for_Audio_Deepfake_Detection

공식 코드

Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset

https://github.com/Yux1angJi/DIFF

공식 코드

Diffusion Features to Bridge Domain Gap for Semantic Segmentation

https://github.com/Yuxuan-Wen/Sim2real-Guidewire-Seg

공식 코드

Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy

https://github.com/ZHKKKe/DualStudent

공식 코드

Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning

https://github.com/ZJULearning/ALDA

공식 코드

Adversarial-Learned Loss for Domain Adaptation

https://github.com/ZJULearning/CariFaceParsing

공식 코드

Weakly-supervised Caricature Face Parsing through Domain Adaptation

https://github.com/ZJULearning/MaxSquareLoss

공식 코드

Domain Adaptation for Semantic Segmentation with Maximum Squares Loss

https://github.com/ZPDu/Boosting-Object-Detection-with-Zero-Shot-Day-Night-Domain-Adaptation

공식 코드

Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation

https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios

공식 코드

Domain-General Crowd Counting in Unseen Scenarios

https://github.com/ZPdesu/MindTheGap

공식 코드

Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks

https://github.com/Zeng-WH/DOP-Tuning

공식 코드

Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization

https://github.com/ZhiwenShao/ADLD

공식 코드

Unconstrained Facial Action Unit Detection via Latent Feature Domain

https://github.com/Zhongying-Deng/DIDA

공식 코드

Dynamic Instance Domain Adaptation

https://github.com/ZitongYu/DeepFAS

공식 코드

Deep Learning for Face Anti-Spoofing: A Survey

https://github.com/a-abedi/euda

공식 코드

EUDA: An Efficient Unsupervised Domain Adaptation via Self-Supervised Vision Transformer

https://github.com/a514514772/DISE-Domain-Invariant-Structure-Extraction

공식 코드

All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation

https://github.com/a791702141/SSG

공식 코드

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

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