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Orthogonal Convolutional Neural Networks
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. ...
A promising solution is to impose orthogonality on convolutional filters. ...
The authors thank Xudong Wang for discussions on filter similarity, Jesse Livezey for the pointer to a previous proof for row-column orthogonality equivalence, and anonymous reviewers for their insightful ...
doi:10.1109/cvpr42600.2020.01152
dblp:conf/cvpr/WangCCY20
fatcat:5ps7bi4x4bfenkfpbapkvjntzm
Orthogonal Convolutional Neural Networks
[article]
2020
arXiv
pre-print
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Our code is publicly available at https://github.com/samaonline/Orthogonal-Convolutional-Neural-Networks. ...
Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. ...
The authors thank Xudong Wang for discussions on filter similarity, Jesse Livezey for the pointer to a previous proof for row-column orthogonality equivalence, Haoran Guo, Ryan Zarcone, and Pratik Sachdeva ...
arXiv:1911.12207v3
fatcat:ebt5b3zeonhjxhiahs3eo6f6ty
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation
[article]
2016
arXiv
pre-print
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Convolutional neural networks (CNNs) have yielded the excellent performance in a variety of computer vision tasks, where CNNs typically adopt a similar structure consisting of convolution layers, pooling ...
In this paper, we propose to apply a novel method, namely Hybrid Orthogonal Projection and Estimation (HOPE), to CNNs in order to introduce orthogonality into the CNN structure. ...
However, more widely used neural models in computer vision, i.e. convolutional neural networks (DCNNs), have not been considered. ...
arXiv:1606.05929v4
fatcat:hkg7tqif4vhizcblj2nvfocb5y
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
[article]
2023
arXiv
pre-print
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Imposing orthogonality on the layers of neural networks is known to facilitate the learning by limiting the exploding/vanishing of the gradient; decorrelate the features; improve the robustness. ...
This paper studies the theoretical properties of orthogonal convolutional layers.We establish necessary and sufficient conditions on the layer architecture guaranteeing the existence of an orthogonal convolutional ...
For Convolutional Neural Networks (LeCun and Bengio, 1995; Krizhevsky et al., 2012; Zhang et al., 2015) , the introduction of the orthogonality constraint is a way to improve the neural network in several ...
arXiv:2108.05623v3
fatcat:yovzqgoembdjzj7pnwxnjgbvja
Orthogonal Spin Current Injected Magnetic Tunnel Junction for Convolutional Neural Networks
[article]
2023
arXiv
pre-print
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Using this concept, we develop a hybrid device-circuit simulation platform to design a network that realizes multiple functionalities of a convolutional neural network. ...
We propose that a spin Hall effect driven magnetic tunnel junction device can be engineered to provide a continuous change in the resistance across it when injected with orthogonal spin currents. ...
Convolutional neural networks (CNN) are a class of artificial neural networks (ANNs) [4] which produces excellent performance in machine learning problems dealing with image data [5] , computer vision ...
arXiv:2207.14603v3
fatcat:yfrfll6qpve33l7ksjpoqdfpem
Convolutional unitary or orthogonal recurrent neural networks
[article]
2023
arXiv
pre-print
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Recurrent neural networks are extremely powerful yet hard to train. ...
The computational complexity of parametrizing this subspace of orthogonal transformations is thus the same as the networks' iteration. ...
Arjovsky, Martin, Amar Shah, and Yoshua Bengio ( 2016 ) "Unitary evolution recurrent neural networks." International Conference on Machine Learning. PMLR, 2016. ...
arXiv:2302.07396v1
fatcat:ydv4joyz4bgbthgqz3yvfvnm5u
Debiasing Convolutional Neural Networks via Meta Orthogonalization
[article]
2020
arXiv
pre-print
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In this work, we tackle the problem of debiasing convolutional neural networks (CNNs) in such instances. ...
labels) to be orthogonal to one another in activation space while maintaining strong downstream task performance. ...
Conclusion In this work, we propose Meta Orthogonalization as a way to debias convolutional neural networks by pushing image concepts to be orthogonal to a learned bias direction. ...
arXiv:2011.07453v1
fatcat:ovkxydhiurg6hce5etodvpku4u
Convolutional neural network on three orthogonal planes for dynamic texture classification
2018
Pattern Recognition
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In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. ...
In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y, xt and y t . ...
The benefit of ensemble models for neural networks was revealed in [6] . ...
doi:10.1016/j.patcog.2017.10.030
fatcat:uzaei56qkvesbidyxunsrd55sm
Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dual-stream Convolutional Neural Network
[article]
2019
arXiv
pre-print
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In this paper, we propose a multilayer fusion approach by means of a pair of shared parameters (dual-stream) convolutional neural network where each network accepts RGB data and a novel colour-based texture ...
descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OC-LBCP) for periocular recognition in the wild. ...
Since 2012, Convolutional Neural Network (CNN) has gained an exponential attention to learn high-dimensional data in the computer vision domain [13] . ...
arXiv:1902.06383v2
fatcat:dieahdhfzfcwbb3w4btcv2b5gm
Orthogonal Features Extraction Method and Its Application in Convolution Neural Network
2021
Shanghai Jiaotong Daxue xuebao
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In view of feature redundancy in the convolutional neural network, the concept of orthogonal vectors is introduced into features. ...
Then, a method for orthogonal features extraction of convolutional neural network is proposed from the perspective of enhancing the differences between features. ...
doi:10.16183/j.cnki.jsjtu.2020.276
doaj:c26cb9e64006431094a72c85908b7096
fatcat:pf2deaaqzvcbdhtu5ddlz4qc44
Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex
[article]
2019
bioRxiv
pre-print
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Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. ...
The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. ...
CaffeNet is an 8-layer convolutional neural network (CNNs) with five convolutional layers and three fully connected layers. ...
doi:10.1101/555193
fatcat:wvoht36gf5hbtpmhtt6c5vkn3y
Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex
2020
Scientific Reports
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Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. ...
We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with neural representations. ...
The bottom layers of the network follow conventional convolutional neural network architecture, consisting of chained convolutional operations followed by max pooling. ...
doi:10.1038/s41598-020-59175-0
pmid:32051467
pmcid:PMC7016009
fatcat:bbmlefja3rbihfck4oefinf7b4
Massive Machine Type Communication using Non-Orthogonal Multiple Access with Convolutional Neural Network Approach
2022
International Journal on Electrical Engineering and Informatics
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In this paper, we propose a Convolutional Neural Network (CNN) approach to decode information from multiple CH without performing traditional communication signal processing. ...
The 5G cellular network supports massive Machine Type Communication (mMTC) for Wireless Sensor Network (WSN) application. ...
This can be overcome by using machine learning, specifically the Convolutional Neural Network (CNN). ...
doi:10.15676/ijeei.2022.14.1.8
fatcat:pmoeqnvtxbex7gyzjxagqitgsi
Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks
2018
International Journal of Computer Assisted Radiology and Surgery
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We present a novel approach to localize partially inserted needles in 3D ultrasound volume with high precision using convolutional neural networks. ...
For patch classification, each voxel is classified from locally extracted raw data of three orthogonal planes centered on it. ...
In our recently published work in training convolutional neural networks (CNN), substantial improvement has been shown to the detection accuracy of needle voxels in 3D US data [10] . ...
doi:10.1007/s11548-018-1798-3
pmid:29855770
pmcid:PMC6132402
fatcat:bh3ns5xsszfjnbp4wprjxisazi
Wearable Airbag System for Real-Time Bicycle Rider Accident Recognition by Orthogonal Convolutional Neural Network (O-CNN) Model
2021
Electronics
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In this paper, similar methods of artificial intelligence (NN, PNN, CNN, PNN-CNN) to are compared to the orthogonal convolutional neural network (O-CNN) method in terms of the performance of judgment accuracy ...
The artificial neural networks were applied to the airbag system and verified the reliability and judgment in advance. ...
In this paper, the artificial neural network designed based on the above method is defined as an orthogonal convolutional neural network (O-CNN). ...
doi:10.3390/electronics10121423
fatcat:3u7aso7z5fg65ccwe4e4mjemmq
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