ArticleAdvances in neural information processing systems2020
Texture Interpolation for Probing Visual Perception.
Article in Advances in neural information processing systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Perceptual misalignment of texture representations in convolutional neural networks.Journal of vision · 2026Article
- Multi-texture synthesis through signal responsive neural cellular automata.Scientific reports · 2025Article
- Neuronal and Behavioral Responses to Naturalistic Texture Images in Macaque Monkeys.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Efficient coding of natural images in the mouse visual cortex.Nature communications · 2024Article
- UNSUPERVISED VIDEO SEGMENTATION ALGORITHMS BASED ON FLEXIBLY REGULARIZED MIXTURE MODELS.Proceedings. International Conference on Image Processing · 2022Article
- Flexibly regularized mixture models and application to image segmentation.Neural networks : the official journal of the International Neural Network Society · 2022Article
- Opposing effects of selectivity and invariance in peripheral vision.Nature communications · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (
Identifiers
36420050PMC9681139W3103273804What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.