Dropout-style feature-detector disabling that reduces overfitting and unlocks more reliable neural network generalization.
The method trains a neural network by using a probabilistic switch to randomly disable feature detectors (units) in selected layers for each training example, then normalizes the contributions when applying the trained network to test data. Technically, it creates an ensemble effect from many thinned sub-networks, typically driven by fixed drop probabilities, rather than relying on a single deterministic model. Previously, using this regularization strategy at scale often required licensing or access to proprietary training techniques from organizations like the assignee.
Build generalization-focused image and text classifiers that train with dropout regularization without licensing fees.
A system for training a neural network. A switch is linked to feature detectors in at least some of the layers of the neural network. For each training case, the switch randomly selectively disables each of the feature detectors in accordance with a preconfigured probability. The weights from each training case are then normalized for applying the neural network to test data.
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