162 research outputs found
On the Connection Between the Human Visual System and Machine Learning:Exploiting Perceptual Distances in Neural Networks
Immersing the artist and designer in the needs of the clinician: evolving the brief for distraction and stress reduction in a new Child Protection Unit.
Engaging clinicians in the design of new, less stressful spaces in healthcare is an interdisciplinary challenge for artists and designers. The design brief is the primary means of ensuring shared understanding and success criteria for creative projects (Press and Cooper 2003) and highlights ambitions and constraints for the project. Conventionally the brief is prepared by the client and issued to the artist or designer. This assumes that the client knows at the outset how to articulate needs and is able to envisage the outcome. Alternative processes emerging through co-design and interdisciplinary working assume the brief is developed or evolved jointly as part of the process and is focused on the experience of the user. This paper focuses on the evolution of a meaningful brief for a Child Protection Unit in NHS Greater Glasgow & Clyde’s new Royal Hospital for Children. Development of the brief was driven by the art and design team and aimed at opening up mutual understanding with the clinicians. The visual mapping of dialogue between artist, interactive designer and clinicians provides a novel approach to understanding this key stage of the process. Fremantle co-ordinated the paper. Hepburn undertook the fieldwork and provided the analysis. Fremantle structured the paper and co-ordinated reviews with Hamilton and Sands
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). The Local
Interpretable Model-agnostic Explanations (LIME) algorithm is often mistakenly
unified with a more general framework of surrogate explainers, which may lead
to a belief that it is the solution to surrogate explainability. In this paper
we empower the community to "build LIME yourself" (bLIMEy) by proposing a
principled algorithmic framework for building custom local surrogate explainers
of black-box model predictions, including LIME itself. To this end, we
demonstrate how to decompose the surrogate explainers family into
algorithmically independent and interoperable modules and discuss the influence
of these component choices on the functional capabilities of the resulting
explainer, using the example of LIME.Comment: 2019 Workshop on Human-Centric Machine Learning (HCML 2019); 33rd
Conference on Neural Information Processing Systems (NeurIPS 2019),
Vancouver, Canad
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers
Many problems in computer vision have recently been tackled using models
whose predictions cannot be easily interpreted, most commonly deep neural
networks. Surrogate explainers are a popular post-hoc interpretability method
to further understand how a model arrives at a particular prediction. 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. Focusing on images, surrogate
explainers, e.g., LIME, generate a local neighbourhood around a query image by
sampling in an interpretable domain. However, these interpretable domains have
traditionally been derived exclusively from the intrinsic features of the query
image, not taking into consideration the manifold of the data the
non-interpretable model has been exposed to in training (or more generally, the
manifold of real images). This leads to suboptimal surrogates trained on
potentially low probability images. We address this limitation by aligning the
local neighbourhood on which the surrogate is trained with the original
training data distribution, even when this distribution is not accessible. We
propose two approaches to do so, namely (1) altering the method for sampling
the local neighbourhood and (2) using perceptual metrics to convey some of the
properties of the distribution of natural images.Comment: 12 pages, 7 figure
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. The encouraging outcome of the proposed approach is based on the
similarity between the neural mechanisms in the auditory and visual pathways.
Furthermore, we customise one of the metrics which has a psychoacoustically
plausible architecture to account for the peculiarities of sound signals. We
evaluate the effectiveness of our proposed metric and several baseline metrics
using a music dataset, with promising results in terms of the correlation
between the metrics and the perceived quality of audio as rated by human
evaluators
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. Inspiration was usually taken from the human visual perceptual
system and how the system processes different perturbations in order to
replicate to what extent it determines our ability to judge image quality.
While recent works have presented deep neural networks trained to predict human
perceptual quality, very few borrow any intuitions from the human visual
system. To address this, we present PerceptNet, a convolutional neural network
where the architecture has been chosen to reflect the structure and various
stages in the human visual system. We evaluate PerceptNet on various
traditional perception datasets and note strong performance on a number of them
as compared with traditional image quality metrics. We also show that including
a nonlinearity inspired by the human visual system in classical deep neural
networks architectures can increase their ability to judge perceptual
similarity. Compared to similar deep learning methods, the performance is
similar, although our network has a number of parameters that is several orders
of magnitude less
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