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Towards dropout training for convolutional neural networks [PDF]
Neural Networks, 2015 Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial ...
Xiaodong Gu
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Augmenting Recurrent Neural Networks Resilience by Dropout
IEEE Transactions on Neural Networks and Learning Systems, 2020 This brief discusses the simple idea that dropout regularization can be used to efficiently induce resiliency to missing inputs at prediction time in a generic neural network. We show how the approach can be effective on tasks where imputation strategies often fail, namely, involving recurrent neural networks and scenarios where whole sequences of ...
Davide Bacciu
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Correlation-based structural dropout for convolutional neural networks
Pattern Recognition, 2021Abstract Convolutional neural networks (CNNs) easily suffer from the over-fitting problem since they are often over-parameterized in the case of small training datasets. The conventional dropout that drops feature units randomly works well for fully connected networks, but fails to regularize CNNs well due to high spatial correlation of the ...
Shu-Tao Xia
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Automatic Dropout for Deep Neural Networks
Lecture Notes in Computer Science, 2020A greater demand for accuracy and performance in neural networks has led to deeper networks with a large number of parameters. Overfitting is a major problem for such deeper networks. Dropout is a popular regularization strategy used in deep neural networks to mitigate overfitting.
Veena Dodballapur +3 more
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Revisiting spatial dropout for regularizing convolutional neural networks
Multimedia Tools and Applications, 2020Overfitting is one of the most challenging problems in deep neural networks with a large number of trainable parameters. To prevent networks from overfitting, the dropout method, which is a strong regularization technique, has been widely used in fully-connected neural networks. In several state-of-the-art convolutional neural network architectures for
Sanghun Lee, Chulhee Lee
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Dropout algorithms for recurrent neural networks
Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, 2018In the last decade, hardware advancements have allowed for neural networks to become much larger in size. Dropout is a popular deep learning technique which has shown to improve the performance of large neural networks. Recurrent neural networks are powerful networks specialised at solving problems which use time series data. Three different approaches
Nathan Watt, Mathys C. du Plessis
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Dropout for Recurrent Neural Networks
2019Neural networks are computational structures which can be trained to perform tasks based on training examples or patterns. Recurrent neural networks are a type of network designed to process time-series data. Dropout is a neural network regularization technique.
Nathan Watt, Mathys C. du Plessis
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Controlled dropout: A different approach to using dropout on deep neural network
2017 IEEE International Conference on Big Data and Smart Computing (BigComp), 2017Deep neural networks (DNNs), which show outstanding performance in various areas, consume considerable amounts of memory and time during training. Our research led us to propose a controlled dropout technique with the potential of reducing the memory space and training time of DNNs.
ByungSoo Ko +3 more
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