Papers Single-Source Domain Generalization
“Single-Source Domain Generalization” 태그가 달린 논문 57편 · 필터 해제
SleepBand: Single-Source Domain Generalization for Sleep Staging via Physiologically Structured Spectral Modeling
Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalization (DG) methods often rely on multiple source domains or domain labels that are rarely available in practice. We tackle …
Single-Source Domain GeneralizationFrequency Adapter with SAM for Generalized Medical Image Segmentation
Medical image segmentation is a critical task in computer-aided diagnosis and treatment planning. However, deep learning models often struggle to generalize across datasets due to domain shifts arising from variations in…
Single-Source Domain GeneralizationMedical Image SegmentationDecoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentation
Domain generalization in fundus imaging is challenging due to variations in acquisition conditions across devices and clinical settings. The inability to adapt to these variations causes performance degradation on unseen…
Single-Source Domain GeneralizationImage SegmentationGranular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting
Single-source domain generalization for crowd counting is highly challenging because a single labeled source domain may contain heterogeneous latent domains, while unseen target domains often exhibit severe distribution …
Single-Source Domain GeneralizationCrowd CountingSpectral Property-Driven Data Augmentation for Hyperspectral Single-Source Domain Generalization
While hyperspectral images (HSI) benefit from numerous spectral channels that provide rich information for classification, the increased dimensionality and sensor variability make them more sensitive to distributional di…
Single-Source Domain GeneralizationData AugmentationHuman Knowledge Integrated Multi-modal Learning for Single Source Domain Generalization
Generalizing image classification across domains remains challenging in critical tasks such as fundus image-based diabetic retinopathy (DR) grading and resting-state fMRI seizure onset zone (SOZ) detection. When domains …
Single-Source Domain GeneralizationImage ClassificationSemantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain (e.g., CT) and directly apply it to a different domain (e.g., MR) witho…
Single-Source Domain GeneralizationMedical Image SegmentationAngioDG: Interpretable Channel-informed Feature-modulated Single-source Domain Generalization for Coronary Vessel Segmentation in X-ray Angiography
Cardiovascular diseases are the leading cause of death globally, with X-ray Coronary Angiography (XCA) as the gold standard during real-time cardiac interventions. Segmentation of coronary vessels from XCA can facilitate…
Single-Source Domain GeneralizationFully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation
Although SAM-based single-source domain generalization models for medical image segmentation can mitigate the impact of domain shift on the model in cross-domain scenarios, these models still face two major challenges. F…
Single-Source Domain GeneralizationMedical Image SegmentationDealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations
Data augmentation is a promising tool for enhancing out-of-distribution generalization, where the key is to produce diverse, challenging variations of the source domain via costly targeted augmentations that maximize its…
Data AugmentationDomain GeneralizationOut-of-Distribution GeneralizationSingle-Source Domain GeneralizationPseudo Multi-Source Domain Generalization: Bridging the Gap Between Single and Multi-Source Domain Generalization
Deep learning models often struggle to maintain performance when deployed on data distributions different from their training data, particularly in real-world applications where environmental conditions frequently change…
Data AugmentationDomain GeneralizationSingle-Source Domain GeneralizationStyle TransferPEER pressure: Model-to-Model Regularization for Single Source Domain Generalization
Data augmentation is a popular tool for single source domain generalization, which expands the source domain by generating simulated ones, improving generalization on unseen target domains. In this work, we show that the…
Data AugmentationDomain GeneralizationmodelModel Selection+1Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation
Despite domain generalization (DG) has significantly addressed the performance degradation of pre-trained models caused by domain shifts, it often falls short in real-world deployment. Test-time adaptation (TTA), which a…
Domain AdaptationDomain GeneralizationGraph MatchingImage Segmentation+4Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data
Automated knee cartilage segmentation using point-of-care ultrasound devices and deep-learning networks has the potential to enhance the management of knee osteoarthritis. However, segmentation algorithms often struggle …
Domain GeneralizationSegmentationSingle-Source Domain GeneralizationColor-Quality Invariance for Robust Medical Image Segmentation
Single-source domain generalization (SDG) in medical image segmentation remains a significant challenge, particularly for images with varying color distributions and qualities. Previous approaches often struggle when mod…
Domain GeneralizationImage SegmentationMedical Image SegmentationSegmentation+2Avoiding Shortcuts: Enhancing Channel-Robust Specific Emitter Identification via Single-Source Domain Generalization
By extracting radio frequency (RF) fingerprints from received signals, specific emitter identification (SEI) becomes a promising technique for physical layer identification of wireless devices. Recently, channel-robust S…
Contrastive LearningDomain GeneralizationSingle-Source Domain GeneralizationRethinking domain generalization in medical image segmentation: One image as one domain
Domain shifts in medical image segmentation, particularly when data comes from different centers, pose significant challenges. Intra-center variability, such as differences in scanner models or imaging protocols, can cau…
DisentanglementDomain GeneralizationImage SegmentationMedical Image Segmentation+4Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification
Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of remote sensing. However, existing approa…
DiversityDomain Generalizationimage-classificationImage Classification+3TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction
We consider the problem of single-source domain generalization. Existing methods typically rely on extensive augmentations to synthetically cover diverse domains during training. However, they struggle with semantic shif…
Domain GeneralizationSingle-Source Domain GeneralizationDRIFTS: Optimizing Domain Randomization with Synthetic Data and Weight Interpolation for Fetal Brain Tissue Segmentation
Fetal brain tissue segmentation in magnetic resonance imaging (MRI) is a crucial tool that supports understanding of neurodevelopment, yet it faces challenges due to the heterogeneity of data coming from different scanne…
Domain GeneralizationImage SegmentationSemantic SegmentationSingle-Source Domain Generalization+1