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This paper proposes an architecture for deep neural networks with hiddenlayer branches that learn targets of lower hierarchy than final layer targets.The branches provide a channel for enforcing useful information in hidden layerwhich helps in attaining better accuracy, both for the final layer and hiddenlayers. The shared layers modify their weights using the gradients of all costfunctions higher than the branching layer. This model provides a flexibleinference system with many levels of targets which is modular and can be usedefficiently in situations requiring different levels of results according tocomplexity. This paper applies the idea to a text classification task on 20Newsgroups data set with two level of hierarchical targets and a comparison ismade with training without the use of hidden layer branches.

Making Sense of Hidden Layer Information in Deep Networks by Learning Hierarchical Targets