Visual Commensence for Scene Understanding
Lead Research Organisation:
University of Glasgow
Department Name: College of Medical, Veterinary, Life Sci
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
People |
ORCID iD |
| Philippe Schyns (Principal Investigator) |
Publications
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
Chen C
(2024)
Cultural facial expressions dynamically convey emotion category and intensity information.
in Current biology : CB
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.)
Ince R
(2017)
Measuring Multivariate Redundant Information with Pointwise Common Change in Surprisal
in Entropy
Ince RA
(2021)
Bayesian inference of population prevalence.
in eLife
Jack RE
(2017)
Toward a Social Psychophysics of Face Communication.
in Annual review of psychology
Jaworska K
(2020)
Healthy aging delays the neural processing of face features relevant for behavior by 40 ms.
in Human brain mapping
Jaworska K
(2022)
Different computations over the same inputs produce selective behavior in algorithmic brain networks.
in eLife
Liu M
(2022)
Facial expressions elicit multiplexed perceptions of emotion categories and dimensions.
in Current biology : CB
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.)
Rychlowska M
(2017)
Functional Smiles: Tools for Love, Sympathy, and War.
in Psychological science
Schyns P
(2020)
Revealing the information contents of memory within the stimulus information representation framework
in Philosophical Transactions of the Royal Society B: Biological Sciences
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 : CB
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
| 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 |