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
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
(2022)
Different computations over the same inputs produce selective behavior in algorithmic brain networks.
in eLife
Jaworska K
(2020)
Healthy aging delays the neural processing of face features relevant for behavior by 40 ms.
in Human brain mapping
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
| 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 |