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Found 15 papers, 2 papers with code
An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
Human-Computer Interaction has been shown to lead to improvements in machine learning systems by boosting model performance, accelerating learning and building user confidence.
Evaluating Perceptual Distances by Fitting Binomial Distributions to Two-Alternative Forced Choice Data
The two-alternative forced choice (2AFC) experimental setup is popular in the visual perception literature, where practitioners aim to understand how human observers perceive distances within triplets that consist of a reference image and two distorted versions of that image.
Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data
Perceptual metrics are traditionally used to evaluate the quality of natural signals, such as images and audio.
What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics
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.
Disentangling the Link Between Image Statistics and Human Perception
Moreover, the direct evaluation of the hypothesis was limited by the inability of the classical image models to deliver accurate estimates of the probability.
Identification, explanation and clinical evaluation of hospital patient subtypes
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 UK teaching hospital.
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems
Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life.
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components
Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable.
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers
By training a simple, more interpretable model to locally approximate the decision boundary of a non-interpretable system, we can estimate the relative importance of the input features on the prediction.
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization
Here we present the \emph{Convolutional RBIG}: an extension that alleviates this issue by imposing that the rotation in RBIG is a convolution.
On the relation between statistical learning and perceptual distances
Since machine learning relies on the statistics of training data as well, the above connection has interesting implications when using perceptual distances (which mimic the behavior of the human visual system) as a loss function.
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception
We generate explanations for images in the Imagenet-C dataset and demonstrate how using a perceptual distances in the surrogate explainer creates more coherent explanations for the distorted and reference images.
bLIMEy: Surrogate Prediction Explanations Beyond LIME
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 of data (images, text and tabular), are model-agnostic and are post-hoc (i. e., can be retrofitted).
PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations.
Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance
While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked.
