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Improving neural networks by preventing co …
Improving neural networks by preventingco- adaptation of feature detectorsG. E. Hinton , N. srivastava , A. Krizhevsky, I. Sutskever and R. R. SalakhutdinovDepartment of Computer Science, University of Toronto,6 King s College Rd, Toronto, Ontario M5S 3G4, Canada To whom correspondence should be addressed; E-mail: a large feedforward neural network is trained on a small training set,it typically performs poorly on held-out test data. This overfitting is greatlyreduced by randomly omitting half of the feature detectors on each trainingcase. This prevents complex co-adaptations in which a feature detector is onlyhelpful in the context of several other specific feature detectors. Instead, eachneuron learns to detect a feature that is generally helpful for producing thecorrect answer given the combinatorially large variety of internal contexts inwhich it must operate. Random dropout gives big improvements on manybenchmark tasks and sets new records for speech and object feedforward, artificial neural network uses layers of non-linear hidden units betweenits inputs and its outputs.
Improving neural networks by preventing co-adaptation of feature detectors G. E. Hinton , N. Srivastava, A. Krizhevsky, I. Sutskever and R. R. Salakhutdinov
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