16 citations · 38 across the 9 of their papers we have counts for
9 papers
What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics
Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez +2
In this study, we investigate the feasibility of utilizing state-of-the-art image perceptual metrics for evaluating audio signals by representing them as spectrograms. The encourag…
Image Statistics Predict the Sensitivity of Perceptual Quality Metrics
Alexander Hepburn, Valero Laparra, Raúl Santos-Rodriguez +1
Previously, Barlow and Attneave hypothesised a link between biological vision and information maximisation. Following Shannon, information was defined using the probability of natu…
Identification, explanation and clinical evaluation of hospital patient subtypes
Enrico Werner, Jeffrey N. Clark, Ranjeet S. Bhamber +5
We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large U…
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers
Ricardo Kleinlein, Alexander Hepburn, Raúl Santos-Rodríguez +1
Many problems in computer vision have recently been tackled using models whose predictions cannot be easily interpreted, most commonly deep neural networks. Surrogate explainers ar…
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization
Valero Laparra, Alexander Hepburn, J. Emmanuel Johnson +1
In this paper we elaborate an extension of rotation-based iterative Gaussianization, RBIG, which makes image Gaussianization possible. Although RBIG has been successfully applied t…
On the relation between statistical learning and perceptual distances
Alexander Hepburn, Valero Laparra, Raul Santos-Rodriguez +2
It has been demonstrated many times that the behavior of the human visual system is connected to the statistics of natural images. Since machine learning relies on the statistics o…
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception
Alexander Hepburn, Raul Santos-Rodriguez
Explaining the decisions of models is becoming pervasive in the image processing domain, whether it is by using post-hoc methods or by creating inherently interpretable models. Whi…
bLIMEy: Surrogate Prediction Explanations Beyond LIME
Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez +1
Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type…
PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Alexander Hepburn, Valero Laparra, Jesús Malo +2
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was us…