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Domain Adaptation

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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/zhaohengyuan1/sct

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SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels

https://github.com/zhaorui-tan/GMDG_cvpr2024

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Rethinking Multi-domain Generalization with A General Learning Objective

https://github.com/zhaorui-tan/L-Reg_NeurIPS24

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Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification

https://github.com/zhaorui-tan/gmdg

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Rethinking Multi-domain Generalization with A General Learning Objective

https://github.com/zhaowanqing/FCN-LP

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Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph

https://github.com/zhaoxin94/awesome-domain-adaptation

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A Review of Single-Source Deep Unsupervised Visual Domain Adaptation

https://github.com/zhaoxin94/awsome-domain-adaptation

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A Survey of Unsupervised Deep Domain Adaptation

https://github.com/zheangh/dfg

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Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting

https://github.com/zhengshuchen/samamba

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SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection

https://github.com/zhengxxn/uda-knn

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Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

https://github.com/zhengzangw/DoPrompt

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Prompt Vision Transformer for Domain Generalization

https://github.com/zhengzangw/PCS-FUDA

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Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation

https://github.com/zheninghuang/nha12d-crack-detection-dataset-and-comparison-study

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NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms

https://github.com/zhihou7/batchformer

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BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

https://github.com/zhijin-ge/stm

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Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer

https://github.com/zhijing-jin/icm4nlp

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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP

https://github.com/zhmeishi/dg-nu

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Domain Generalization via Nuclear Norm Regularization

https://github.com/zhongying-deng/bort2

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Robust Target Training for Multi-Source Domain Adaptation

https://github.com/zhongying-deng/dac-net

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Domain Attention Consistency for Multi-Source Domain Adaptation

https://github.com/zhoujiahuan1991/cvpr2025-scap

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SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting

https://github.com/zhouzheyuan/sgm3d

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SGM3D: Stereo Guided Monocular 3D Object Detection

https://github.com/zhu-han/ASR-Adaption-Class-Similarity

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Domain Adaptation Using Class Similarity for Robust Speech Recognition

https://github.com/zhu-han/castle

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Boosting Cross-Domain Speech Recognition with Self-Supervision

https://github.com/zhu-xlab/unida

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Universal Domain Adaptation for Remote Sensing Image Scene Classification

https://github.com/zhunzhong07/ECN

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Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification

https://github.com/zhuoxiao-chen/cevt

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Conditional Extreme Value Theory for Open Set Video Domain Adaptation

https://github.com/zhuoxiao-chen/mos

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MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection

https://github.com/zhuoxiao-chen/redb-da-3ddet

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Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling

https://github.com/zhuxing0/SR-Stereov1-DAPE

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SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo Matching

https://github.com/zhuzhu804/bba

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Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation

https://github.com/zhyczy/fed-co2

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Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning

https://github.com/zhyhan/RDA

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Towards Accurate and Robust Domain Adaptation under Noisy Environments

https://github.com/zhyhan/transpar

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Learning Transferable Parameters for Unsupervised Domain Adaptation

https://github.com/zhyx12/mixlrco

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Low-confidence Samples Matter for Domain Adaptation

https://github.com/zichengpan/eianet

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EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles

https://github.com/zijunli7/condo

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ConDo: Continual Domain Expansion for Absolute Pose Regression

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