Improving neural networks by preventing co-adaptation of feature detectors

3 Jul 2012Geoffrey E. HintonNitish SrivastavaAlex KrizhevskyIlya SutskeverRuslan R. Salakhutdinov

When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Image Classification CIFAR-10 Improving neural networks by preventing co-adaptation of feature detectors Percentage correct 84.4 # 78

Methods used in the Paper


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