Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.
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A systematic literature review of explainability in multimodal attention models finds most studies focus on vision-language tasks with attention-based explanations, but evaluation methods lack consistency and modality-specific considerations.
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.
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Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models
A systematic literature review of explainability in multimodal attention models finds most studies focus on vision-language tasks with attention-based explanations, but evaluation methods lack consistency and modality-specific considerations.