Results 21 to 30 of about 9,700,127 (369)
Graph Condensation via Receptive Field Distribution Matching [PDF]
arXiv.org, 2022 Graph neural networks (GNNs) enable the analysis of graphs using deep learning, with promising results in capturing structured information in graphs. This paper focuses on creating a small graph to represent the original graph, so that GNNs trained on ...
Mengyang Liu +3 more
semanticscholar +1 more source
Perceptual Extreme Super Resolution Network with Receptive Field Block [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 Perceptual Extreme Super-Resolution for single image is extremely difficult, because the texture details of different images vary greatly. To tackle this difficulty, we develop a super resolution network with receptive field block based on Enhanced SRGAN.
Taizhang Shang +4 more
semanticscholar +1 more source
Multi-Branch Cascade Receptive Field Residual Network
IEEE Access, 2023 Deep convolutional neural networks (CNNs) have significantly enhanced image classification in the past decade. This paper proposes Multi-branch Cascade Receptive Field Residual Networks (MCRF-ResNets) based on the original Residual Network (ResNet ...
Xudong Zhang, Wenjie Liu, Guoqing Wu
doaj +1 more source
Feedback Generates a Second Receptive Field in Neurons of Visual Cortex
Nature, 2020 Animals sense the environment through pathways that link sensory organs to the brain. In the visual system, these feedforward pathways define the classical feedforward receptive field (ffRF), the area in space in which visual stimuli excite a neuron 1 ...
Andreas J. Keller +2 more
semanticscholar +2 more sources
Frontiers in Cellular Neuroscience, 2021 It was well documented that both the size of the dendritic field and receptive field of retinal ganglion cells (RGCs) are developmentally regulated in the mammalian retina, and visual stimulation is required for the maturation of the dendritic and ...
Hui Chen +4 more
doaj +1 more source
The Information Content of Receptive Fields [PDF]
Neuron, 2003 The nervous system must observe a complex world and produce appropriate, sometimes complex, behavioral responses. In contrast to this complexity, neural responses are often characterized through very simple descriptions such as receptive fields or tuning curves.
Thomas L. Adelman +2 more
openaire +3 more sources
Orientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields [PDF]
Journal of Computational Neuroscience, 2025, 2023 This paper presents a theoretical analysis of the orientation selectivity of simple and complex cells that can be well modelled by the generalized Gaussian derivative model for visual receptive fields, with the purely spatial component of the receptive fields determined by oriented affine Gaussian derivatives for different orders of spatial ...
arxiv +1 more source
RF-Net: An End-To-End Image Matching Network Based on Receptive Field [PDF]
Computer Vision and Pattern Recognition, 2019 This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable matching framework is desirable and challenging. The very recent approach, LF-
Xuelun Shen +7 more
semanticscholar +1 more source
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification [PDF]
European Signal Processing Conference, 2019 Convolutional Neural Networks (CNNs) have had great success in many machine vision as well as machine audition tasks. Many image recognition network architectures have consequently been adapted for audio processing tasks. However, despite some successes,
Khaled Koutini +3 more
semanticscholar +1 more source
Receptive Field Structures for Recognition [PDF]
Neural Computation, 2006 Localized operators, like Gabor wavelets and difference-of-gaussian filters, are considered useful tools for image representation. This is due to their ability to form a sparse code that can serve as a basis set for high-fidelity reconstruction of natural images.
Pawan Sinha, Benjamin Balas
openaire +3 more sources