Tag: deep learning
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Unsupervised Denoising of Low-Dose Liver CT with Attention
Improve low-dose liver CT images with unsupervised denoising using perceptual attention networks for clearer, noise-free clinical scans.
DAPPr: Possibilistic Uncertainty for Reliable Deep Learning
Discover DAPPr, a novel framework improving epistemic uncertainty quantification in deep learning for more reliable predictions on unseen data.
REALM: Cross-Modal RGB & Event Data Alignment Framework
Discover REALM, a cross-modal framework aligning RGB and event data for enhanced perception using low-rank adaptation and pretrained RGB models.
Do Sparse Autoencoders Effectively Capture Concept Manifolds?
Explore how sparse autoencoders capture concept manifolds and the implications for representation learning and AI interpretability.
DEFault++: Automated Fault Diagnosis for Transformers
DEFault++ offers precise fault detection, categorization, and diagnosis for transformer models, boosting AI reliability and developer accuracy.