On the public citation graph

Noise-robust recognition of objects by humans and deep neural networks

https://doi.org/10.1101/2020.08.03.234625

0 signed · 67 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1002/9781119170174.epcn201openalex-snapshot

Foundations of Vision

inferredcites →https://doi.org/10.1007/978-3-030-28954-6_10openalex-snapshot

Layer-Wise Relevance Propagation: An Overview

inferredcites →https://doi.org/10.1007/978-3-030-58580-8_4openalex-snapshot

A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions

inferredcites →https://doi.org/10.1007/bf00344251openalex-snapshot

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot

ImageNet Large Scale Visual Recognition Challenge

inferredcites →https://doi.org/10.1016/j.cell.2019.04.005openalex-snapshot

Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences

inferredcites →https://doi.org/10.1016/j.cub.2017.07.068openalex-snapshot

Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes

inferredcites →https://doi.org/10.1016/j.neuroimage.2009.06.060openalex-snapshot

Accurate and robust brain image alignment using boundary-based registration

inferredcites →https://doi.org/10.1016/j.neuron.2008.10.043openalex-snapshot

Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey

inferredcites →https://doi.org/10.1016/j.neuron.2012.01.010openalex-snapshot

How Does the Brain Solve Visual Object Recognition?

inferredcites →https://doi.org/10.1016/j.neuron.2012.10.014openalex-snapshot

A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain

inferredcites →https://doi.org/10.1016/j.neuron.2018.03.044openalex-snapshot

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy

inferredcites →https://doi.org/10.1016/j.preteyeres.2006.01.002openalex-snapshot

Eye movements and the control of actions in everyday life

inferredcites →https://doi.org/10.1016/s0042-6989(97)00273-3openalex-snapshot

External noise distinguishes attention mechanisms

inferredcites →https://doi.org/10.1016/s1053-8119(02)91132-8openalex-snapshot

Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

inferredcites →https://doi.org/10.1037//0096-1523.25.4.1016openalex-snapshot

Robust representations for faces: Evidence from visual search.

inferredcites →https://doi.org/10.1038/14819openalex-snapshot

Hierarchical models of object recognition in cortex

inferredcites →https://doi.org/10.1038/46027openalex-snapshot

Signal but not noise changes with perceptual learning

inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot

Human-level control through deep reinforcement learning

inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot

Deep learning

inferredcites →https://doi.org/10.1038/nature21056openalex-snapshot

Dermatologist-level classification of skin cancer with deep neural networks

inferredcites →https://doi.org/10.1038/ncomms15037openalex-snapshot

Generic decoding of seen and imagined objects using hierarchical visual features

inferredcites →https://doi.org/10.1038/nn.4244openalex-snapshot

Using goal-driven deep learning models to understand sensory cortex

inferredcites →https://doi.org/10.1038/nn1444openalex-snapshot

Decoding the visual and subjective contents of the human brain

inferredcites →https://doi.org/10.1038/nrn2314openalex-snapshot

Interpreting fMRI data: maps, modules and dimensions

inferredcites →https://doi.org/10.1038/s41586-020-2350-5openalex-snapshot

A map of object space in primate inferotemporal cortex

inferredcites →https://doi.org/10.1038/s41593-019-0392-5openalex-snapshot

Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior

inferredcites →https://doi.org/10.1073/pnas.0700622104openalex-snapshot

A feedforward architecture accounts for rapid categorization

inferredcites →https://doi.org/10.1073/pnas.092277599openalex-snapshot

Rapid natural scene categorization in the near absence of attention

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.1719397115openalex-snapshot

Recurrent computations for visual pattern completion

inferredcites →https://doi.org/10.1073/pnas.1905544116openalex-snapshot

Recurrence is required to capture the representational dynamics of the human visual system

inferredcites →https://doi.org/10.1093/cercor/7.2.181openalex-snapshot

Retinotopic organization in human visual cortex and the spatial precision of functional MRI

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2016.485openalex-snapshot

Improving the Robustness of Deep Neural Networks via Stability Training

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/icccn.2017.8038465openalex-snapshot

A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions

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/10.1111/j.1467-7687.2005.00402.xopenalex-snapshot

Object identification in preschool children and adults

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/10.1126/science.1145183openalex-snapshot

Meaning in Visual Search

inferredcites →https://doi.org/10.1126/science.aav9436openalex-snapshot

Neural population control via deep image synthesis

inferredcites →https://doi.org/10.1137/040616024openalex-snapshot

A Review of Image Denoising Algorithms, with a New One

inferredcites →https://doi.org/10.1145/1961189.1961199openalex-snapshot

LIBSVM

inferredcites →https://doi.org/10.1145/2733373.2807412openalex-snapshot

MatConvNet

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1146/annurev-vision-082114-035447openalex-snapshot

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing

inferredcites →https://doi.org/10.1146/annurev.neuro.30.051606.094238openalex-snapshot

Mechanisms of Face Perception

inferredcites →https://doi.org/10.1148/radiol.2019191293openalex-snapshot

Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation

inferredcites →https://doi.org/10.1152/jn.00127.2013openalex-snapshot

How attention extracts objects from noise

inferredcites →https://doi.org/10.1162/neco.1989.1.4.541openalex-snapshot

Backpropagation Applied to Handwritten Zip Code Recognition

inferredcites →https://doi.org/10.1162/neco.1995.7.1.108openalex-snapshot

Training with Noise is Equivalent to Tikhonov Regularization

inferredcites →https://doi.org/10.1167/18.10.903openalex-snapshot

Can deep learning networks acquire the robustness of human recognition when faced with objects in visual noise?

inferredcites →https://doi.org/10.1364/josaa.4.002379openalex-snapshot

Relations between the statistics of natural images and the response properties of cortical cells

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.pone.0130140openalex-snapshot

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

inferredcites →https://doi.org/10.1523/jneurosci.0388-18.2018openalex-snapshot

Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks

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/10.3758/app.72.5.1205openalex-snapshot

Current perspectives in medical image perception

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot

Explaining and Harnessing Adversarial Examples

inferredcites →https://doi.org/10.48550/arxiv.1808.08750openalex-snapshot

Generalisation in humans and deep neural networks

inferredcites →https://doi.org/10.7554/elife.40224openalex-snapshot

Bayesian analysis of retinotopic maps

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-18256-3_45openalex-snapshot

A Method for Automatic Monoplane Angiography Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-29126-5_6openalex-snapshot

Deep Robust Neural Networks Inspired by Human Cognitive Bias Against Transfer-based Attacks

inferred← citeshttps://doi.org/10.1145/3649457openalex-snapshot

Man and the Machine: Effects of AI-assisted Human Labeling on Interactive Annotation of Real-time Video Streams

inferred← citeshttps://doi.org/10.1167/jov.21.10.14openalex-snapshot

Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.