Internal node bagging: an explicit ensemble learning method in neural network training
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We introduce a novel view to understand how dropout works as an inexplicit ensemble learning method, which do not point out how many and which nodes to learn a certain feature. We propose a new training method named internal node bagging, this method explicitly force a group of nodes to learn a certain feature in training time, and combine those nodes to be one node in inference time. It means we can use much more parameters to improve model's fitting ability in training time while keeping model small in inference time. We test our method on several benchmark datasets and find it significantly more efficiency than dropout on small model.
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