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
(2024)
Cultural facial expressions dynamically convey emotion category and intensity information.
in Current biology : CB
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
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
Liu M
(2022)
Facial expressions elicit multiplexed perceptions of emotion categories and dimensions.
in Current biology : CB
Pichon S
(2021)
Emotion perception in habitual players of action video games.
in Emotion (Washington, D.C.)
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
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.)
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 |