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DEEPCEPTION

Visual perception in deep neural networks

Total Cost €

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EC-Contrib. €

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Partnership

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Project "DEEPCEPTION" data sheet

The following table provides information about the project.

Coordinator
KATHOLIEKE UNIVERSITEIT LEUVEN 

Organization address
address: OUDE MARKT 13
city: LEUVEN
postcode: 3000
website: www.kuleuven.be

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Belgium [BE]
 Project website https://klab.lt
 Total cost 258˙530 €
 EC max contribution 258˙530 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-GF
 Starting year 2016
 Duration (year-month-day) from 2016-10-01   to  2019-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) coordinator 258˙530.00
2    MASSACHUSSETTS INSTITUTE OF TECHNOLOGY MIT CORPORATION US (CAMBRIDGE) partner 0.00

Map

 Project objective

How do we recognize what we see? Despite the deceptive ease of perceiving things, explaining how we see turns out to be a supremely difficult task. Only recently advances in computer vision finally brought a class of models, known as deep neural nets, that are capable of matching human performance in several visual perception tasks. In this project, we aim to employ the knowledge how human visual system processes visual information in order to critically evaluate and improve the existing models of vision. Our aim is twofold. On the one hand, little is known yet how well deep nets can account for a huge variety of tasks that human visual system faces daily. We will perform a broad battery of tests in order to shed light on the power of deep nets and to spot potential limitations. Capitalizing on these shortcomings, in the second part of this project we aim to improve the existing technology by introducing novel algorithms based on behavioral and neural data of humans. Taken together, this project will lay a solid foundation for the psychologically- and biologically-based development of the next generation of deep nets.

 Publications

year authors and title journal last update
List of publications.
2019 Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B. Issa, James J. DiCarlo
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
published pages: 974-983, ISSN: 1097-6256, DOI: 10.1038/s41593-019-0392-5
Nature Neuroscience 22/6 2020-03-17
2019 Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo
Brain-like object recognition with high-performing shallow recurrent ANNs
published pages: , ISSN: , DOI:
Advances in Neural Information Systems 32 (NeurIPS 2019) 2020-03-17
2017 Chengxu Zhuang, Jonas Kubilius, Mitra JZ Hartmann, Daniel L. Yamins
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
published pages: 2555--2565, ISSN: , DOI:
Advances in Neural Information Processing Systems 30 (NIPS 2017) 2020-03-17
2018 Jonas Kubilius
Predict, then simplify
published pages: 110-111, ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2017.12.006
NeuroImage 180 2020-03-17
2018 Jonas Kubilius, Martin Schrimpf, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo
CORnet: Modeling the Neural Mechanisms of Core Object Recognition
published pages: , ISSN: , DOI: 10.1101/408385
bioRxiv 2020-03-17
2018 Jonas Kubilius, Kohitij Kar, Kailyn Schmidt, James J. DiCarlo
Can deep neural networks rival human ability to generalize in core object recognition?
published pages: , ISSN: , DOI:
Conference on Cognitive Computational Neuroscience 2020-03-17
2018 Kohitij Kar, Jonas Kubilius, Kailyn M. Schmidt, Elias B. Issa, James J. DiCarlo
Evidence that recurrent circuits are critical to the ventral stream\'s execution of core object recognition behavior
published pages: , ISSN: , DOI: 10.1101/354753
bioRxiv 2020-03-17
2018 Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Daniel L. K. Yamins, and James J. DiCarlo
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
published pages: , ISSN: , DOI: 10.1101/407007
bioRxiv 2020-03-17
2018 Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. Yamins
Task-Driven Convolutional Recurrent Models of the Visual System
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems 31 (NIPS 2018) 2020-03-17

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