Results 11 to 20 of about 3,882,996 (203)
Canonical convolutional neural networks
2022 International Joint Conference on Neural Networks (IJCNN), 2023 33983405We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products.
Wolter, Moritz +3 more
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Convolutional Neural Networks: A Survey
Computers, 2023 Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including ...
Moez Krichen
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Pansharpening by Convolutional Neural Networks [PDF]
Remote Sensing, 2016 A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem.
Giuseppe Masi +3 more
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Differential convolutional neural network
Neural Networks, 2019 PubMedID: 31125914Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing similar artificial
Avci M. +5 more
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Powerset convolutional neural networks
CoRR, 2019 We present a novel class of convolutional neural networks (CNNs) for set functions,i.e., data indexed with the powerset of a finite set. The convolutions are derivedas linear, shift-equivariant functions for various notions of shifts on set functions.The
Püschel, Markus +2 more
core +5 more sources
Contextual Convolutional Neural Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost ...
Ionut Cosmin Duta +2 more
openaire +3 more sources
Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks [PDF]
, 2016 We investigate a human-machine collaborative drawing environment in which an autonomous agent sketches images while optionally allowing a user to directly influence the agent's trajectory.
Akten, Memo, Grierson, Mick
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Cloud-based video analytics using convolutional neural networks. [PDF]
, 2018 Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management.
Anjum, Ashiq +3 more
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Pointwise Convolutional Neural Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 10 pages, 6 figures, 10 tables.
Binh-Son Hua +2 more
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Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Maosheng Yang, Elvin Isufi, Geert Leus
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