Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
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Daniel L. K. Yamins | Ha Hong | Charles F. Cadieu | Diego Ardila | Najib J. Majaj | James J. DiCarlo | Nicolas Pinto | Ethan A. Solomon | J. DiCarlo | N. Pinto | Daniel Yamins | C. Cadieu | N. Majaj | Ha Hong | Diego Ardila | E. Solomon | Nicolas Pinto
[1] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[2] D. Hubel,et al. Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.
[3] J. Movshon,et al. The statistical reliability of signals in single neurons in cat and monkey visual cortex , 1983, Vision Research.
[4] R. Desimone,et al. Stimulus-selective properties of inferior temporal neurons in the macaque , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[5] L. Weiskrantz,et al. Impairments of visual object transforms in monkeys. , 1984, Brain : a journal of neurology.
[6] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[7] David I. Perrett,et al. Neurophysiology of shape processing , 1993, Image Vis. Comput..
[8] Keiji Tanaka,et al. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.
[9] G. Orban,et al. How task-related are the responses of inferior temporal neurons? , 1995, Visual Neuroscience.
[10] Denis Fize,et al. Speed of processing in the human visual system , 1996, Nature.
[11] Bartlett W. Mel. SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.
[12] E. Rolls,et al. INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.
[13] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[14] Keiji Tanaka,et al. Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. , 1998, Journal of neurophysiology.
[15] Bernhard Schölkopf,et al. The connection between regularization operators and support vector kernels , 1998, Neural Networks.
[16] W. Newsome,et al. The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.
[17] S. Thorpe,et al. Rapid categorization of natural images by rhesus monkeys , 1998, Neuroreport.
[18] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[19] D. Coppola,et al. Idiosyncratic characteristics of saccadic eye movements when viewing different visual environments , 1999, Vision Research.
[20] Bernhard Schölkopf,et al. Regularization Networks and Support Vector Machines , 2000 .
[21] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[22] P. Fldik,et al. The Speed of Sight , 2001, Journal of Cognitive Neuroscience.
[23] N. Sigala,et al. Visual categorization shapes feature selectivity in the primate temporal cortex , 2002, Nature.
[24] Edmund T. Rolls,et al. Invariant Object Recognition in the Visual System with Novel Views of 3D Objects , 2002, Neural Computation.
[25] M. Behrmann,et al. Impact of learning on representation of parts and wholes in monkey inferotemporal cortex , 2002, Nature Neuroscience.
[26] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[27] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[28] R. Quian Quiroga,et al. Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering , 2004, Neural Computation.
[29] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[30] J. Gallant,et al. Complete functional characterization of sensory neurons by system identification. , 2006, Annual review of neuroscience.
[31] Keiji Tanaka,et al. Neuronal Responses to Object Images in the Macaque Inferotemporal Cortex at Different Stimulus Discrimination Levels , 2006, The Journal of Neuroscience.
[32] Mikio L. Braun,et al. Accurate Error Bounds for the Eigenvalues of the Kernel Matrix , 2006, J. Mach. Learn. Res..
[33] David G. Lowe,et al. University of British Columbia. , 1945, Canadian Medical Association journal.
[34] Peter Földiák,et al. Bayesian binning for maximising information rate of rapid serial presentation for sensory neurons , 2007, BMC Neuroscience.
[35] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Thomas Serre,et al. A quantitative theory of immediate visual recognition. , 2007, Progress in brain research.
[37] David D. Cox,et al. Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .
[38] H. Komatsu,et al. Effects of task demands on the responses of color-selective neurons in the inferior temporal cortex , 2007, Nature Neuroscience.
[39] Delbert Dueck,et al. Clustering by Passing Messages Between Data Points , 2007, Science.
[40] A. Torralba,et al. The role of context in object recognition , 2007, Trends in Cognitive Sciences.
[41] R. Rifkin,et al. Notes on Regularized Least Squares , 2007 .
[42] Joachim M. Buhmann,et al. On Relevant Dimensions in Kernel Feature Spaces , 2008, J. Mach. Learn. Res..
[43] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[44] Nikolaus Kriegeskorte,et al. Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .
[45] Keiji Tanaka,et al. Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.
[46] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[47] David D. Cox,et al. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation , 2009, PLoS Comput. Biol..
[48] Nicole C. Rust,et al. Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT , 2010, The Journal of Neuroscience.
[49] C. Baker,et al. Informativeness and learning: Response to Gauthier and colleagues , 2010, Trends in Cognitive Sciences.
[50] K. Koepsell,et al. Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies , 2010, Proceedings of the National Academy of Sciences.
[51] DiCarlo James,et al. Human versus machine: comparing visual object recognition systems on a level playing field. , 2010 .
[52] J. DiCarlo,et al. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex , 2010, Neuron.
[53] Eero P. Simoncelli,et al. Metamers of the ventral stream , 2011, Nature Neuroscience.
[54] Berin Martini,et al. NeuFlow: A runtime reconfigurable dataflow processor for vision , 2011, CVPR 2011 WORKSHOPS.
[55] Klaus-Robert Müller,et al. Kernel Analysis of Deep Networks , 2011, J. Mach. Learn. Res..
[56] Stéphane Mallat,et al. Group Invariant Scattering , 2011, ArXiv.
[57] Nicolas Pinto,et al. Comparing state-of-the-art visual features on invariant object recognition tasks , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).
[58] Michèle Fabre-Thorpe,et al. The Characteristics and Limits of Rapid Visual Categorization , 2011, Front. Psychology.
[59] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[60] N. Kriegeskorte,et al. Categorical, Yet Graded – Single-Image Activation Profiles of Human Category-Selective Cortical Regions , 2012, The Journal of Neuroscience.
[61] Klaus-Robert Müller,et al. Deep Boltzmann Machines and the Centering Trick , 2012, Neural Networks: Tricks of the Trade.
[62] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[63] Konrad P. Körding,et al. Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons , 2012, PLoS Comput. Biol..
[64] Matthew T. Kaufman,et al. Neural population dynamics during reaching , 2012, Nature.
[65] Marc'Aurelio Ranzato,et al. Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[66] Ha Hong,et al. Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream , 2013, NIPS.
[67] Ha Hong,et al. The Neural Representation Benchmark and its Evaluation on Brain and Machine , 2013, ICLR.
[68] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[69] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[70] R. Fergus,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[71] Brad Wyble,et al. Detecting meaning in RSVP at 13 ms per picture , 2013, Attention, perception & psychophysics.