Results 11 to 20 of about 3,683,270 (301)
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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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., Sarıgül M., Ozyildirim B.M.
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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
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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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A noise robust convolutional neural network for image classification
Results in Engineering, 2021 Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks.
Mohammad Momeny +4 more
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Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
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Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network
Inventions, 2021 In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications ...
Elena Solovyeva, Ali Abdullah
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INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks.
R. Niessner, H. Schilling, B. Jutzi
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Simplified Routing Mechanism for Capsule Networks
Algorithms, 2023 Classifying digital images using neural networks is one of the most fundamental tasks within the field of artificial intelligence. For a long time, convolutional neural networks have proven to be the most efficient solution for processing visual data ...
János Hollósi +2 more
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Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction
IEEE Access, 2022 As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem.
Yunqing Zhang, Yuming Ma, Yihui Liu
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