Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units

Dropout is a simple yet effective technique to improve generalization performance and prevent overfitting in deep neural networks (DNNs). In this paper, we discuss three novel observations about dropout to better understand the generalization of DNNs with rectified linear unit (ReLU) activations: 1) dropout is a smoothing technique that encourages each local linear model of a DNN to be trained on data points from nearby regions; 2) a constant dropout rate can result in effective neural-deactivation rates that are significantly different for layers with different fractions of activated neurons; and 3) the rescaling factor of dropout causes an inconsistency to occur between the normalization during training and testing conditions when batch normalization is also used... (read more)

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METHOD TYPE
Dropout
Regularization
Batch Normalization
Normalization