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Found 6 papers, 1 papers with code
Counterfactual Explanations via Riemannian Latent Space Traversal
The adoption of increasingly complex deep models has fueled an urgent need for insight into how these models make predictions.
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
Our experiments on a real-world clinical dataset show the potential of using unsupervised methods for fetal brain anomaly detection.
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health.
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements.
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Via a novel inpainting-based modification we spatially limit the changes made with no extra inference step, encouraging the removal of spatially constrained shortcut features while ensuring that the shortcut-free counterfactuals preserve their remaining image features to a high degree.
Style Decomposition for Improved Neural Style Transfer
Universal Neural Style Transfer (NST) methods are capable of performing style transfer of arbitrary styles in a style-agnostic manner via feature transforms in (almost) real-time.
