Research graph
References from Perceptual misalignment of texture representations in convolutional neural networks. Local targets link to admitted publications; unresolved targets remain external evidence.
Neural population control via deep image synthesis
10.1126/science.aav9436 · 2019 · External reference
Incorporating long-range consistency in CNN-based texture generation
2017 · External reference
The texture lexicon: understanding the categorization of visual texture terms and their relationship to texture images
10.1207/s15516709cog2102_4 · 1997 · External reference
Deep convolutional models improve predictions of macaque v1 responses to natural images
10.1371/journal.pcbi.1006897 · 2019 · External reference
Deep neural networks rival the representation of primate it cortical neurons
2014 · External reference
On perceptual analyzers underlying visual texture discrimination: Part I
10.1007/bf00337138 · 1978 · External reference
Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes
10.7554/elife.72081 · 2021 · External reference
Model metamers reveal divergent invariances between biological and artificial neural networks
10.1038/s41593-023-01442-0 · 2023 · External reference
Metamers of the ventral stream
10.1038/nn.2889 · 2011 · External reference
A functional and perceptual signature of the second visual area in primates
10.1038/nn.3402 · 2013 · External reference
Texture synthesis using convolutional neural networks
2015 · External reference
Partial success in closing the gap between human and machine vision
2021 · External reference
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
10.1523/jneurosci.5023-14.2015 · 2015 · External reference
A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex
10.1523/jneurosci.1822-22.2023 · 2023 · External reference
Variance predicts salience in central sensory processing
10.7554/elife.03722 · 2014 · External reference
Visual pattern discrimination
10.1109/tit.1962.1057698 · 1962 · External reference
Textons, the elements of texture perception, and their interactions
10.1038/290091a0 · 1981 · External reference
Deep supervised, but not unsupervised, models may explain it cortical representation
2014 · External reference
Representational similarity analysis — connecting the branches of systems neuroscience
2008 · External reference
From BoW to CNN: Two decades of texture representation for texture classification
10.1007/s11263-018-1125-z · 2019 · External reference
Preattentive texture discrimination with early vision mechanisms
10.1364/josaa.7.000923 · 1990 · External reference
Unsupervised learning of mid-level visual representations
10.1016/j.conb.2023.102834 · 2024 · External reference
Unraveling the complexity of rat object vision requires a full convolutional network and beyond
10.1016/j.patter.2024.101149 · 2025 · External reference
Prune and distill: Similar reformatting of image information along rat visual cortex and deep neural networks
10.52202/068431-2190 · 2022 · External reference
Mouse visual cortex as a limited resource system that self-learns an ecologicallygeneral representation
10.1371/journal.pcbi.1011506 · 2023 · External reference
Entropy and inference, revisited
2002 · External reference
Gradual development of visual texture-selective properties between macaque areas V2 and V4
2017 · External reference
Scikit-learn: Machine learning in Python
2011 · External reference
A parametric texture model based on joint statistics of complex wavelet coefficients
10.1023/a:1026553619983 · 2000 · External reference
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
10.1523/jneurosci.0388-18.2018 · 2018 · External reference
Integrative benchmarking to advance neurally mechanistic models of human intelligence
10.1016/j.neuron.2020.07.040 · 2020 · External reference
Deep correlations for texture synthesis
10.1145/3015461 · 2017 · External reference
Very deep convolutional networks for large-scale image recognition
2015 · External reference
Diverse deep neural networks all predict human inferior temporal cortex well, after training and fitting
2021 · External reference
Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures
10.7554/elife.54347 · 2020 · External reference
Efficient processing of natural scenes in visual cortex
10.3389/fncel.2022.1006703 · 2022 · External reference
Local statistics in natural scenes predict the saliency of synthetic textures
10.1073/pnas.0914916107 · 2010 · External reference
Texture discrimination by Gabor functions
10.1007/bf00341922 · 1986 · External reference
Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis
2017 · External reference
Texture interpolation for probing visual perception
2020 · External reference
Attention is all you need
2017 · External reference
Local image statistics: Maximum-entropy constructions and perceptual salience
10.1364/josaa.29.001313 · 2012 · External reference
Textures as probes of visual processing
10.1146/annurev-vision-102016-061316 · 2017 · External reference
Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception
10.1126/sciadv.abd4205 · 2020 · External reference
Are deep neural networks adequate behavioral models of human visual perception?
10.1146/annurev-vision-120522-031739 · 2023 · External reference
Performance-optimized hierarchical models predict neural responses in higher visual cortex
10.1073/pnas.1403112111 · 2014 · External reference
Visual processing of informative multipoint correlations arises primarily in v2
10.7554/elife.06604 · 2015 · External reference
Predisposed and learned preferences for multipoint visual statistics in visually naive newly hatched chicks
10.1098/rspb.2025.3157 · 2026 · External reference
Texture discriminability in monkey inferotemporal cortex predicts human texture perception
10.1152/jn.00532.2014 · 2014 · External reference
Exploring texture ensembles by efficient Markov chain Monte Carlo—toward a "trichromacy" theory of texture
10.1109/34.862195 · 2000 · External reference
Selectivity and tolerance for visual texture in macaque v2
10.1073/pnas.1510847113 · 2016 · External reference
Neuronal and behavioral responses to naturalistic texture images in macaque monkeys
10.1523/jneurosci.0349-24.2024 · 2024 · External reference
PyTorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation
2024 · External reference
How well do deep neural networks trained on object recognition characterize the mouse visual system? In
2019 · External reference
Describing textures in the wild
10.1109/cvpr.2014.461 · 2014 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2016 · External reference
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
2019 · External reference
U-attention to textures: Hierarchical hourglass vision transformer for universal texture synthesis
10.1145/3565516.3565525 · 2022 · External reference
Deep residual learning for image recognition
2016 · External reference
A sliced wasserstein loss for neural texture synthesis
2021 · External reference
The origins and prevalence of texture bias in convolutional neural networks
2020 · External reference
MobileNets: efficient convolutional neural networks for mobile vision applications
2017 · External reference
Densely Connected Convolutional Networks
2017 · External reference
Shape or texture: Understanding discriminative features in CNNs
2021 · External reference
Perceptual losses for real-time style transfer and super-resolution
10.1007/978-3-319-46475-6_43 · 2016 · External reference
Learning multiple layers of features from tiny images.
2009 · External reference
CORnet: Modeling the neural mechanisms of core object recognition
10.1101/408385 · 2018 · External reference
Unresolved reference
2017 · External reference
Texture synthesis through convolutional neural networks and spectrum constraints
2016 · External reference
Unresolved reference
2003 · External reference
Unresolved reference
2021 · External reference
Learning transferable visual models from natural language supervision
2021 · External reference
Unresolved reference
2018 · External reference
Unresolved reference
2017 · External reference
Rethinking the inception architecture for computer vision
2016 · External reference
Unresolved reference
2016 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2019 · External reference
Perceptual losses for real-time style transfer and super-resolution
10.1007/978-3-319-46475-6_43 · ExternalCitation · doi-reference
On perceptual analyzers underlying visual texture discrimination: Part I
10.1007/bf00337138 · ExternalCitation · doi-reference
Texture discrimination by Gabor functions
10.1007/bf00341922 · ExternalCitation · doi-reference
From BoW to CNN: Two decades of texture representation for texture classification
10.1007/s11263-018-1125-z · ExternalCitation · doi-reference
Unsupervised learning of mid-level visual representations
10.1016/j.conb.2023.102834 · ExternalCitation · doi-reference
Integrative benchmarking to advance neurally mechanistic models of human intelligence
10.1016/j.neuron.2020.07.040 · ExternalCitation · doi-reference
Unraveling the complexity of rat object vision requires a full convolutional network and beyond
10.1016/j.patter.2024.101149 · ExternalCitation · doi-reference
A parametric texture model based on joint statistics of complex wavelet coefficients
10.1023/a:1026553619983 · ExternalCitation · doi-reference
Textons, the elements of texture perception, and their interactions
10.1038/290091a0 · ExternalCitation · doi-reference
Metamers of the ventral stream
10.1038/nn.2889 · ExternalCitation · doi-reference
A functional and perceptual signature of the second visual area in primates
10.1038/nn.3402 · ExternalCitation · doi-reference
Model metamers reveal divergent invariances between biological and artificial neural networks
10.1038/s41593-023-01442-0 · ExternalCitation · doi-reference
Local statistics in natural scenes predict the saliency of synthetic textures
10.1073/pnas.0914916107 · ExternalCitation · doi-reference
Performance-optimized hierarchical models predict neural responses in higher visual cortex
10.1073/pnas.1403112111 · ExternalCitation · doi-reference
Selectivity and tolerance for visual texture in macaque v2
10.1073/pnas.1510847113 · ExternalCitation · doi-reference
Predisposed and learned preferences for multipoint visual statistics in visually naive newly hatched chicks
10.1098/rspb.2025.3157 · ExternalCitation · doi-reference
CORnet: Modeling the neural mechanisms of core object recognition
10.1101/408385 · ExternalCitation · doi-reference
Exploring texture ensembles by efficient Markov chain Monte Carlo—toward a "trichromacy" theory of texture
10.1109/34.862195 · ExternalCitation · doi-reference
Describing textures in the wild
10.1109/cvpr.2014.461 · ExternalCitation · doi-reference
Visual pattern discrimination
10.1109/tit.1962.1057698 · ExternalCitation · doi-reference
Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception
10.1126/sciadv.abd4205 · ExternalCitation · doi-reference
Neural population control via deep image synthesis
10.1126/science.aav9436 · ExternalCitation · doi-reference
Deep correlations for texture synthesis
10.1145/3015461 · ExternalCitation · doi-reference
U-attention to textures: Hierarchical hourglass vision transformer for universal texture synthesis
10.1145/3565516.3565525 · ExternalCitation · doi-reference
Textures as probes of visual processing
10.1146/annurev-vision-102016-061316 · ExternalCitation · doi-reference
Are deep neural networks adequate behavioral models of human visual perception?
10.1146/annurev-vision-120522-031739 · ExternalCitation · doi-reference
Texture discriminability in monkey inferotemporal cortex predicts human texture perception
10.1152/jn.00532.2014 · ExternalCitation · doi-reference
The texture lexicon: understanding the categorization of visual texture terms and their relationship to texture images
10.1207/s15516709cog2102_4 · ExternalCitation · doi-reference
Local image statistics: Maximum-entropy constructions and perceptual salience
10.1364/josaa.29.001313 · ExternalCitation · doi-reference
Preattentive texture discrimination with early vision mechanisms
10.1364/josaa.7.000923 · ExternalCitation · doi-reference
Deep convolutional models improve predictions of macaque v1 responses to natural images
10.1371/journal.pcbi.1006897 · ExternalCitation · doi-reference
Mouse visual cortex as a limited resource system that self-learns an ecologicallygeneral representation
10.1371/journal.pcbi.1011506 · ExternalCitation · doi-reference
Neuronal and behavioral responses to naturalistic texture images in macaque monkeys
10.1523/jneurosci.0349-24.2024 · ExternalCitation · doi-reference
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
10.1523/jneurosci.0388-18.2018 · ExternalCitation · doi-reference
A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex
10.1523/jneurosci.1822-22.2023 · ExternalCitation · doi-reference
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
10.1523/jneurosci.5023-14.2015 · ExternalCitation · doi-reference
Efficient processing of natural scenes in visual cortex
10.3389/fncel.2022.1006703 · ExternalCitation · doi-reference
Prune and distill: Similar reformatting of image information along rat visual cortex and deep neural networks
10.52202/068431-2190 · ExternalCitation · doi-reference
Variance predicts salience in central sensory processing
10.7554/elife.03722 · ExternalCitation · doi-reference
Visual processing of informative multipoint correlations arises primarily in v2
10.7554/elife.06604 · ExternalCitation · doi-reference
Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures
10.7554/elife.54347 · ExternalCitation · doi-reference
Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes
10.7554/elife.72081 · ExternalCitation · doi-reference
