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
Liu M
(2022)
Facial expressions elicit multiplexed perceptions of emotion categories and dimensions
in Current Biology
Yan Y
(2023)
Strength of predicted information content in the brain biases decision behavior
in Current Biology
Zhan J
(2019)
Dynamic Construction of Reduced Representations in the Brain for Perceptual Decision Behavior.
in Current biology : CB
Zhan J
(2021)
Modeling individual preferences reveals that face beauty is not universally perceived across cultures.
in Current biology : CB
Chen C
(2024)
Cultural facial expressions dynamically convey emotion category and intensity information.
in Current biology : CB
Ince RA
(2021)
Bayesian inference of population prevalence.
in eLife
Jaworska K
(2022)
Different computations over the same inputs produce selective behavior in algorithmic brain networks.
in eLife
Pichon S
(2021)
Emotion perception in habitual players of action video games.
in Emotion (Washington, D.C.)
Ince R
(2017)
Measuring Multivariate Redundant Information with Pointwise Common Change in Surprisal
in Entropy
Jaworska K
(2020)
Healthy aging delays the neural processing of face features relevant for behavior by 40 ms.
in Human brain mapping
Nölle J
(2021)
Facial expressions of emotion include iconic signals of rejection and acceptance
in Journal of Vision
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.)
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
Chen C
(2018)
Distinct facial expressions represent pain and pleasure across cultures
in Proceedings of the National Academy of Sciences
Rychlowska M
(2017)
Functional Smiles: Tools for Love, Sympathy, and War
in Psychological Science
Snoek L
(2023)
Testing, explaining, and exploring models of facial expressions of emotions.
in Science advances
Schyns PG
(2022)
Degrees of algorithmic equivalence between the brain and its DNN models.
in Trends in cognitive sciences
| 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 |