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Found 8 papers, 2 papers with code
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models
In this paper, we employ information-theory tools to investigate this phenomenon, revealing a fundamental tradeoff between uncertainty and perception.
Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models
Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades.
Semi-supervised Quality Evaluation of Colonoscopy Procedures
By integrating the local metric over the withdrawal phase, we build a global, offline quality metric, which is shown to be highly correlated to the standard Polyp Per Colonoscopy (PPC) quality metric.
BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning
Training a generative model on a single image has drawn significant attention in recent years.
Deep Self-Dissimilarities as Powerful Visual Fingerprints
Features extracted from deep layers of classification networks are widely used as image descriptors.
Sparsity Aware Normalization for GANs
Generative adversarial networks (GANs) are known to benefit from regularization or normalization of their critic (discriminator) network during training.
Dense xUnit Networks
For example, on ImageNet, our DxNet outperforms a ReLU-based DenseNet having 30% more parameters and achieves state-of-the-art results for this budget of parameters.
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of parameters.
