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Currently, deep neural networks are the state of the art on problems such asspeech recognition and computer vision. In this extended abstract, we show thatshallow feed-forward networks can learn the complex functions previouslylearned by deep nets and achieve accuracies previously only achievable withdeep models. Moreover, in some cases the shallow neural nets can learn thesedeep functions using a total number of parameters similar to the original deepmodel. We evaluate our method on the TIMIT phoneme recognition task and areable to train shallow fully-connected nets that perform similarly to complex,well-engineered, deep convolutional architectures. Our success in trainingshallow neural nets to mimic deeper models suggests that there probably existbetter algorithms for training shallow feed-forward nets than those currentlyavailable.

Do Deep Nets Really Need to be Deep?