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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/lijin118/ATM

공식 코드

Maximum Density Divergence for Domain Adaptation

https://github.com/lijin118/CGDM

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Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation

https://github.com/lijin118/LPJT

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Locality Preserving Joint Transfer for Domain Adaptation

https://github.com/lijuntaopku/UFD

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Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model

https://github.com/lil-lab/qa-from-hf

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Continually Improving Extractive QA via Human Feedback

https://github.com/lileicv/PDEN

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Progressive Domain Expansion Network for Single Domain Generalization

https://github.com/lilianngweta/pisco

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Simple Disentanglement of Style and Content in Visual Representations

https://github.com/liminxian/DukeMTMC-SI-Tracklet

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Unsupervised Tracklet Person Re-Identification

https://github.com/lindvalllab/bioclinical-modernbert

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BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP

https://github.com/lingeringlight/aloft

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ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization

https://github.com/lingeringlight/domaindrop

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DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization

https://github.com/lingeringlight/placedropout

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PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization

https://github.com/lingeringlight/seta

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SETA: Semantic-Aware Token Augmentation for Domain Generalization

https://github.com/lingeringlight/start

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START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation

https://github.com/lingrayy/plpb

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Robust Source-Free Domain Adaptation for Fundus Image Segmentation

https://github.com/links-ads/igarss-fmars

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FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models

https://github.com/links-ads/igarss-spada

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Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery

https://github.com/lins-lab/fedbr

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FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

https://github.com/lins-lab/ttab

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On Pitfalls of Test-Time Adaptation

https://github.com/lisadunlap/alia

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Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation

https://github.com/lisadunlap/lads

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Using Language to Extend to Unseen Domains

https://github.com/liulu112601/URT

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A Universal Representation Transformer Layer for Few-Shot Image Classification

https://github.com/liuquande/FedDG-ELCFS

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FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

https://github.com/liuquande/SAML

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Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains

https://github.com/livreq/imda

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Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation

https://github.com/liweiyangv/dpcl

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Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning

https://github.com/lixiaotong97/dsu

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Modeling Uncertain Feature Representation for Domain Generalization

https://github.com/lixinustc/samb-transformeruda

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Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation

https://github.com/liy140/multidomain-measextract-corpus

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Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context Extraction

https://github.com/liyiying/Feature_Critic

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Feature-Critic Networks for Heterogeneous Domain Generalization

https://github.com/liyuhui-12/drmfordg

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Direct-Effect Risk Minimization for Domain Generalization

https://github.com/liyuke65535/part-aware-transformer

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Part-Aware Transformer for Generalizable Person Re-identification

https://github.com/liyunsheng13/BDL

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Bidirectional Learning for Domain Adaptation of Semantic Segmentation

https://github.com/liyunsheng13/DRT

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Dynamic Transfer for Multi-Source Domain Adaptation

https://github.com/lizheren/MSVCL_MICCAI2021

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Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

https://github.com/lizhi-ntu/OCKD-FacePAD

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One-Class Knowledge Distillation for Face Presentation Attack Detection

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