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Characterizing the temporal dynamics of object recognition by deep neural networks : role of depth

https://doi.org/10.1101/178541
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/(sici)1097-0193(1998)6:4<316::aid-hbm9>3.0.co;2-6openalex-snapshot
A sequence of object-processing stages revealed by fMRI in the human occipital lobe
inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot
Visualizing and Understanding Convolutional Networks
inferredcites →https://doi.org/10.1007/978-3-540-88682-2_24openalex-snapshot
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot
The Pascal Visual Object Classes (VOC) Challenge
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/0013-4694(83)90135-9openalex-snapshot
A new method for off-line removal of ocular artifact
inferredcites →https://doi.org/10.1016/0013-4694(89)90180-6openalex-snapshot
Spherical splines for scalp potential and current density mapping
inferredcites →https://doi.org/10.1016/s0042-6989(00)00004-3openalex-snapshot
The implementation of visual routines
inferredcites →https://doi.org/10.1016/s0042-6989(02)00298-5openalex-snapshot
Surfing a spike wave down the ventral stream
inferredcites →https://doi.org/10.1016/s0166-2236(00)01657-xopenalex-snapshot
The distinct modes of vision offered by feedforward and recurrent processing
inferredcites →https://doi.org/10.1037/e636952013-029openalex-snapshot
The Functional Organization of the Ventral Visual Pathway in Humans
inferredcites →https://doi.org/10.1038/381520a0openalex-snapshot
Speed of processing in the human visual system
inferredcites →https://doi.org/10.1038/srep27755openalex-snapshot
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
inferredcites →https://doi.org/10.1068/p5321openalex-snapshot
A Biologically Inspired Algorithm for the Recovery of Shading and Reflectance Images
inferredcites →https://doi.org/10.1073/pnas.0700622104openalex-snapshot
A feedforward architecture accounts for rapid categorization
inferredcites →https://doi.org/10.1073/pnas.1403112111openalex-snapshot
Performance-optimized hierarchical models predict neural responses in higher visual cortex
inferredcites →https://doi.org/10.1073/pnas.1619788114openalex-snapshot
Theory of cortical function
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/tpami.2006.54openalex-snapshot
Generic object recognition with boosting
inferredcites →https://doi.org/10.1113/jphysiol.1962.sp006837openalex-snapshot
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1167/10.7.992openalex-snapshot
The role of Weibull image statistics in rapid object detection in natural scenes
inferredcites →https://doi.org/10.1167/15.12.376openalex-snapshot
Mapping human visual representations in space and time by neural networks
inferredcites →https://doi.org/10.1167/16.12.501openalex-snapshot
Overlap in performance of CNN's, human behavior and EEG classification
inferredcites →https://doi.org/10.1167/9.4.29openalex-snapshot
Brain responses strongly correlate with Weibull image statistics when processing natural images
inferredcites →https://doi.org/10.1177/0049124104268644openalex-snapshot
Multimodel Inference
inferredcites →https://doi.org/10.1371/journal.pcbi.1002726openalex-snapshot
Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories
inferredcites →https://doi.org/10.1371/journal.pcbi.1003915openalex-snapshot
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
inferredcites →https://doi.org/10.1371/journal.pcbi.1003963openalex-snapshot
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
inferredcites →https://doi.org/10.1523/jneurosci.0559-09.2009openalex-snapshot
Natural Scene Categories Revealed in Distributed Patterns of Activity in the Human Brain
inferredcites →https://doi.org/10.1523/jneurosci.16-22-07376.1996openalex-snapshot
Contextual Modulation in Primary Visual Cortex
inferredcites →https://doi.org/10.1523/jneurosci.3128-13.2013openalex-snapshot
From Image Statistics to Scene Gist: Evoked Neural Activity Reveals Transition from Low-Level Natural Image Structure to Scene Category
inferredcites →https://doi.org/10.1523/jneurosci.5023-14.2015openalex-snapshot
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream
inferredcites →https://doi.org/10.3389/fncom.2014.00168openalex-snapshot
Visual dictionaries as intermediate features in the human brain
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition

What points at this work

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inferred← citeshttps://doi.org/10.1016/j.visres.2020.04.005openalex-snapshot
Time-resolved correspondences between deep neural network layers and EEG measurements in object processing
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