CoordGate: efficiently computing spatially-varying convolutions in convolutional neural networks

CoordGate: efficiently computing spatially-varying convolutions in convolutional neural networks

Optical imaging systems are inherently limited in their resolution due to the point spread function (PSF), which applies a static, yet spatially-varying, convolution to the image. This degradation can be addressed via Convolutional Neural Networks (CNNs), particularly through deblurring techniques....

Bibliografske podrobnosti
Main Authors:	Howard, S, Norreys, P, Döpp, A
Format:	Conference item
Jezik:	English
Izdano:	British Machine Vision Association 2023

Podobne knjige/članki

Convolutional Neural Network with Spatial-Variant Convolution Kernel
od: Yongpeng Dai, et al.
Izdano: (2020-08-01)

Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement
od: Fazal-E -Wahab, et al.
Izdano: (2025-01-01)

Process tomography of structured optical gates with convolutional neural networks
od: Tareq Jaouni, et al.
Izdano: (2024-01-01)

An energy-efficient convolution unit for depthwise separable convolutional neural networks
od: Chong, Yi Sheng, et al.
Izdano: (2021)

Efficient field‐programmable gate array‐based reconfigurable accelerator for deep convolution neural network
od: Xianghong Hu, et al.
Izdano: (2021-03-01)

Gated convolutional neural network for fine-grained automatic essay scoring
od: Lee, Xing Zhao
Izdano: (2017)

Face recognition using the convolutional neural network for barrier gate system
od: Mochammad Langgeng, Prasetyo, et al.
Izdano: (2021)

Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array
od: Yang Yang Lee, et al.
Izdano: (2024-01-01)

Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks
od: Chen, Yu-Hsin, et al.
Izdano: (2016)

Computing nasalance with MFCCs and Convolutional Neural Networks.
od: Andrés Lozano, et al.
Izdano: (2024-01-01)

Learning an Efficient Convolution Neural Network for Pansharpening
od: Yecai Guo, et al.
Izdano: (2019-01-01)

Improved Landslide Deformation Prediction Using Convolutional Neural Network–Gated Recurrent Unit and Spatial–Temporal Data
od: Honglei Yang, et al.
Izdano: (2025-02-01)

Spatial Channel Attention for Deep Convolutional Neural Networks
od: Tonglai Liu, et al.
Izdano: (2022-05-01)

Molecular generation using gated graph convolutional neural networks and reinforcement learning
od: Divyansh, Gupta
Izdano: (2019)

Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit
od: Chuen Yik Kang, et al.
Izdano: (2022-10-01)

Adaptively Tuning a Convolutional Neural Network by Gate Process for Image Denoising
od: Yoonsik Kim, et al.
Izdano: (2019-01-01)

Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks
od: Joaquin Rives Gambin, et al.
Izdano: (2021-01-01)

Automatic plant disease detection using computationally efficient convolutional neural network
od: Muhammad Rizwan, et al.
Izdano: (2024-12-01)

Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks
od: Xianfeng Xu, et al.
Izdano: (2022-04-01)

Gated spatio and temporal convolutional neural network for activity recognition: towards gated multimodal deep learning
od: Novanto Yudistira, et al.
Izdano: (2017-12-01)

Efficient Convolution Neural Networks for Object Tracking Using Separable Convolution and Filter Pruning
od: Yuanhong Mao, et al.
Izdano: (2019-01-01)

An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks
od: Li Hai, et al.
Izdano: (2025-01-01)

All-optical computing based on convolutional neural networks
od: Liao, Kun, et al.
Izdano: (2022)

Techniques in enhancing computation and understanding of convolutional neural networks
od: Abdiyeva, Kamila
Izdano: (2021)

Complexity of Deep Convolutional Neural Networks in Mobile Computing
od: Saad Naeem, et al.
Izdano: (2020-01-01)

Improvement in the Convolutional Neural Network for Computed Tomography Images
od: Keisuke Manabe, et al.
Izdano: (2021-02-01)

On the Reduction of Computational Complexity of Deep Convolutional Neural Networks
od: Partha Maji, et al.
Izdano: (2018-04-01)

A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing
od: Mário P. Véstias
Izdano: (2019-07-01)

Design of energy-efficient convolution neural network accelerator
od: Shao, Yuhan
Izdano: (2025)

A resource-efficient quantum convolutional neural network
od: Yanqi Song, et al.
Izdano: (2024-04-01)

Efficient quantum state tomography with convolutional neural networks
od: Tobias Schmale, et al.
Izdano: (2022-09-01)

SAR Image Classification Using Gated Channel Attention Based Convolutional Neural Network
od: Anjun Zhang, et al.
Izdano: (2023-01-01)

Data-driven modeling of sluice gate flows using a convolutional neural network
od: Xiaohui Yan, et al.
Izdano: (2023-09-01)

Pre‐training of gated convolution neural network for remote sensing image super‐resolution
od: Yali Peng, et al.
Izdano: (2021-04-01)

Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit
od: Ping Xuan, et al.
Izdano: (2019-07-01)

Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis
od: Narisa Zhao, et al.
Izdano: (2021-01-01)

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks
od: Ebtesam Almazrouei, et al.
Izdano: (2021-04-01)

Spatially Recalibrated Convolutional Neural Network for Vehicle Type Recognition
od: Shi Hao Tan, et al.
Izdano: (2023-01-01)

Convolutional Neural Network for Pansharpening with Spatial Structure Enhancement Operator
od: Weiwei Huang, et al.
Izdano: (2021-10-01)

Mixed‐decomposed convolutional network: A lightweight yet efficient convolutional neural network for ocular disease recognition
od: Xiaoqing Zhang, et al.
Izdano: (2024-04-01)