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Found 4 papers, 1 papers with code
Joint Search of Data Augmentation Policies and Network Architectures
In this paper, we propose a joint optimization method for data augmentation policies and network architectures to bring more automation to the design of training pipeline.
ShakeDrop Regularization for Deep Residual Learning
In this paper, to relieve the overfitting effect of ResNet and its improvements (i. e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization.
ShakeDrop regularization
This paper proposes a powerful regularization method named \textit{ShakeDrop regularization}.
Deep Pyramidal Residual Networks with Separated Stochastic Depth
On general object recognition, Deep Convolutional Neural Networks (DCNNs) achieve high accuracy.
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