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Single-Source Domain Generalization

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하위 태스크: 1
평가표: 2
보존된 평가 기록: 17

In this task a model is trained in a single source domain and then it is tested in a number of target domains

코드

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

https://github.com/busername/learning_to_diversify

공식 코드

Learning to Diversify for Single Domain Generalization

https://github.com/cheng-01037/causality-medical-image-domain-generalization

공식 코드

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation

https://github.com/dnap512/SelfReg

공식 코드

SelfReg: Self-supervised Contrastive Regularization for Domain Generalization

https://github.com/explainableml/acvc

공식 코드

Attention Consistency on Visual Corruptions for Single-Source Domain Generalization

https://github.com/kaiseem/slaug

공식 코드

Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation

https://github.com/leekyungmoon/XDED

공식 코드

Cross-Domain Ensemble Distillation for Domain Generalization

https://github.com/liamheng/non-iid_medical_image_segmentation

공식 코드

Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

https://github.com/liangchen527/itta

공식 코드

Improved Test-Time Adaptation for Domain Generalization

https://github.com/nikosefth/crafting-shifts

공식 코드

Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization

https://github.com/qintianjian-lab/ras4dg

공식 코드

Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis

https://github.com/ratschlab/icmseg

공식 코드

Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms

https://github.com/ravishah1/dcin-cqg

공식 코드

Color-Quality Invariance for Robust Medical Image Segmentation

https://github.com/s-enmt/pseudodomainbed

공식 코드

Pseudo Multi-Source Domain Generalization: Bridging the Gap Between Single and Multi-Source Domain Generalization

https://github.com/sarihust/sammed

공식 코드

Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation

https://github.com/shahinakk/lg_sdg

공식 코드

Language Guided Domain Generalized Medical Image Segmentation

https://github.com/shengcheng/aba

공식 코드

Adversarial Bayesian Augmentation for Single-Source Domain Generalization

https://github.com/tejas-gokhale/alt

공식 코드

Improving Diversity with Adversarially Learned Transformations for Domain Generalization

https://github.com/wkklavis/dapsam

공식 코드

Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation

https://github.com/yore0/ttdg-mgm

공식 코드

Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation

https://github.com/zhaohaoyu376/morestyle

공식 코드

MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation

https://github.com/xch-liu/geom-tex-dg

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