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Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream.
Neuronal processing: How fast is the speed of thought?
Martin J Tovée · 1994
Earlier work this paper cites.
The distinct modes of vision offered by feedforward and recurrent processing
Victor AF Lamme and Pieter R Roelfsema · 2000
Earlier work this paper cites.
Top-down facilitation of visual recognition
Moshe Bar, Karim S Kassam, Avniel Singh Ghuman, Jasmine Boshyan, Annette M Schmid, Anders M Dale, Matti S Hämäläinen, Ksenija Marinkovic, Daniel L Schacter, Bruce R Rosen, et al · 2006
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, and Nicole C Rust · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
A century of gestalt psychology in visual perception: I. perceptual grouping and figure–ground organization
Johan Wagemans, James H Elder, Michael Kubovy, Stephen E Palmer, Mary A Peterson, Manish Singh, and Rüdiger von der Heydt · 2012
Earlier work this paper cites.
Hierarchical modular optimization of convolutional networks achieves representations similar to macaque it and human ventral stream
Daniel L Yamins, Ha Hong, Charles Cadieu, and James J DiCarlo · 2013
Earlier work this paper cites.
Deep supervised, but not unsupervised, models may explain it cortical representation
Seyed-Mahdi Khaligh-Razavi and Nikolaus Kriegeskorte · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
Earlier work this paper cites.
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
Umut Güçlü and Marcel AJ van Gerven · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Recurrent convolutional neural network for object recognition
Ming Liang and Xiaolin Hu · 2015
Earlier work this paper cites.
Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance
Najib J Majaj, Ha Hong, Ethan A Solomon, and James J DiCarlo · 2015
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Comparison of object recognition behavior in human and monkey
Rishi Rajalingham, Kailyn Schmidt, and James J DiCarlo · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Radoslaw M Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, and Aude Oliva · 2016
Recurrent convolutional neural networks: a better model of biological object recognition
Courtney J Spoerer, Patrick McClure, and Nikolaus Kriegeskorte · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Later among the works it cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Later among the works it cites.
Feedback networks
Amir R Zamir, Te-Lin Wu, Lin Sun, William B Shen, Bertram E Shi, Jitendra Malik, and Silvio Savarese · 2017
Later among the works it cites.
Learning Transferable Architectures for Scalable Image Recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2017
Later among the works it cites.
Oscillatory dynamics of perceptual to conceptual transformations in the ventral visual pathway
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer · 2016
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Deep neural networks as a computational model for human shape sensitivity
Jonas Kubilius, Stefania Bracci, and Hans P Op de Beeck · 2016
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Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Qianli Liao and Tomaso Poggio · 2016
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Tensorflow-slim image classification model library
N. Silberman and S. Guadarrama · 2016
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Using goal-driven deep learning models to understand sensory cortex
Daniel LK Yamins and James J DiCarlo · 2016
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Alex Clarke, Barry J Devereux, and Lorraine K Tyler · 2018
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Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le · 2018
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Predict, then simplify
Jonas Kubilius · 2018
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Iamnn: Iterative and adaptive mobile neural network for efficient image classification
Sam Leroux, Pavlo Molchanov, Pieter Simoens, Bart Dhoedt, Thomas Breuel, and Jan Kautz · 2018
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Task-driven convolutional recurrent models of the visual system
Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J DiCarlo, and Daniel LK Yamins · 2018
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Beyond core object recognition: Recurrent processes account for object recognition under occlusion
Karim Rajaei, Yalda Mohsenzadeh, Reza Ebrahimpour, and Seyed-Mahdi Khaligh-Razavi · 2018
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Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rishi Rajalingham, Elias B Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, and James J DiCarlo · 2018
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Recurrent computations for visual pattern completion
Hanlin Tang, Martin Schrimpf, William Lotter, Charlotte Moerman, Ana Paredes, J.O. Josue Ortega Caro, Walter Hardesty, David Cox, and Gabriel Kreiman · 2018
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Yuxin Wu and Kaiming He · 2018
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Neural population control via deep image synthesis
Pouya Bashivan, Kohitij Kar, and James J DiCarlo · 2019
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Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B Issa, and James J DiCarlo · 2019
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