Visual Commensence for Scene Understanding
Lead Research Organisation:
University of Glasgow
Department Name: College of Medical, Veterinary, Life Sci
Abstract
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People |
ORCID iD |
| Philippe Schyns (Principal Investigator) |
Publications
Jack RE
(2017)
Toward a Social Psychophysics of Face Communication.
in Annual review of psychology
Rychlowska M
(2017)
Functional Smiles: Tools for Love, Sympathy, and War.
in Psychological science
Ince R
(2017)
Measuring Multivariate Redundant Information with Pointwise Common Change in Surprisal
in Entropy
Chen C
(2018)
Distinct facial expressions represent pain and pleasure across cultures.
in Proceedings of the National Academy of Sciences of the United States of America
Zhan J
(2019)
Dynamic Construction of Reduced Representations in the Brain for Perceptual Decision Behavior.
in Current biology : CB
Jaworska K
(2020)
Healthy aging delays the neural processing of face features relevant for behavior by 40 ms.
in Human brain mapping
Heaven D
(2020)
Why faces don't always tell the truth about feelings.
in Nature
Schyns PG
(2020)
Revealing the information contents of memory within the stimulus information representation framework.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Nölle J
(2021)
Facial expressions of emotion include iconic signals of rejection and acceptance
in Journal of Vision
Pichon S
(2021)
Emotion perception in habitual players of action video games.
in Emotion (Washington, D.C.)
Daube C
(2021)
Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity.
in Patterns (New York, N.Y.)
Zhan J
(2021)
Modeling individual preferences reveals that face beauty is not universally perceived across cultures.
in Current biology : CB
Ince RA
(2021)
Bayesian inference of population prevalence.
in eLife
Liu M
(2022)
Facial expressions elicit multiplexed perceptions of emotion categories and dimensions.
in Current biology : CB
Jaworska K
(2022)
Different computations over the same inputs produce selective behavior in algorithmic brain networks.
in eLife
Schyns PG
(2022)
Degrees of algorithmic equivalence between the brain and its DNN models.
in Trends in cognitive sciences
Snoek L
(2023)
Testing, explaining, and exploring models of facial expressions of emotions.
in Science advances
Yan Y
(2023)
Strength of predicted information content in the brain biases decision behavior
in Current Biology
Bjornsdottir RT
(2024)
Social class perception is driven by stereotype-related facial features.
in Journal of experimental psychology. General
Chen C
(2024)
Cultural facial expressions dynamically convey emotion category and intensity information.
in Current biology : CB
| Description | We have developed a new methodology to achieve a deeper interpretability of deep networks. Specifically, using information theoretic measures, we can now visualize the information that is represented at each layer of a deep network. From this understanding, we can better estimate the information transformation function that are performed across layers. Furthermore, we have using Generational Autoencoders to compare the representations constructed on the hidden layers with those of several other models (i.e. classic ResNet DeepNetwork, an engineered generative model and an ideal observer model. |
| Exploitation Route | Others users of deep networks might use our methodologies to better understand why deep networks fail to generalize--cf. adversarial testing. |
| Sectors | Aerospace Defence and Marine Creative Economy Digital/Communication/Information Technologies (including Software) |
| URL | https://arxiv.org/abs/1811.07807 |