Efficient Training of Convolutional Neural Nets on Large Distributed Systems [PDF]
2018 IEEE International Conference on Cluster Computing (CLUSTER), 2018 Deep Neural Networks (DNNs) have achieved im- pressive accuracy in many application domains including im- age classification. Training of DNNs is an extremely compute- intensive process and is solved using variants of the stochastic gradient descent (SGD) algorithm. A lot of recent research has focussed on improving the performance of DNN training.
Dheeraj Sreedhar +4 more
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, 2019 Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far.
Kisuk Lee +5 more
core +6 more sources
Automatic segmentation of hip cartilage with deep convolutional neural nets for the evaluation of acetabulum and femoral T1ρ and T2 relaxation times. [PDF]
, 2018 Michaël J. A. Girard +4 more
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Door and cabinet recognition using Convolutional Neural Nets and real-time method fusion for handle detection and grasping [PDF]
2017 3rd International Conference on Control, Automation and Robotics (ICCAR), 2017 Adrian Llopart, Ole Ravn, N. Andersen
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CAE-CNN-Based DOA Estimation Method for Low-Elevation-Angle Target
Remote Sensing, 2022 For the DOA (direction of arrival) estimation of a low-elevation-angle target under the influence of a multipath effect, this paper proposes a DOA estimation method based on CAE (convolutional autoencoder) and CNN (convolutional neural network).
Fangzheng Zhao +3 more
doaj +1 more source
Microstrip antenna modelling based on image‐based convolutional neural network
Electronics Letters, 2023 Convolutional neural networks (CNN) have a strong feature extraction ability for images and present a high level of efficiency and accuracy in object detection and image recognition.
Hao Fu +4 more
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CDF‐net: A convolutional neural network fusing frequency domain and spatial domain features
IET Computer Vision, 2023 Convolutional neural network (CNN), as a classic deep learning algorithm, has been applied to various computer vision tasks. However, most classic CNN models focus on the extraction and utilisation of spatial domain features, while ignoring the potential
Aitao Yang +7 more
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.
Liang-Chieh Chen +4 more
semanticscholar +1 more source
Applied Sciences, 2021 Trust in artificial intelligence (AI) predictions is a crucial point for a widespread acceptance of new technologies, especially in sensitive areas like autonomous driving. The need for tools explaining AI for deep learning of images is thus eminent. Our
C. Schorr +3 more
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RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation [PDF]
2021 Digital Image Computing: Techniques and Applications (DICTA), 2021 Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity.
Khan, Tariq M +2 more
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