Dropout is a human-made regularization technique in neural network training that randomly sets a fraction of neuron activations to zero during each forward pass, with the dropout rate controlled by a hyperparameter. By preventing neurons from co-adapting — forcing each to learn robust, independently useful features — dropout reduces overfitting and improves generalization. The technique was introduced by Srivastava et al. (2014) and is implemented as standard operations in all major deep learning frameworks. The mechanism of persistence is the reproduction of dropout implementations in training codebases worldwide and the teaching of dropout as a standard regularization method in machine learning and deep learning curricula. [formal: intermissio | substrate: behavior | horizon: a life | explicit: yes | epoch: 0.01]
Accepted ontology entry
dropout
Dropout is a human-made regularization technique in neural network training that randomly sets a fraction of neuron activations to zero during each forward pass, with the dropout rate controlled by a hyperparameter. By preventing neurons f…
Definition
Why it is in scope
Human-made regularization technique in neural network training: randomly deactivating a fraction of neurons during each training step to prevent co-adaptation and reduce overfitting.
Names and aliases
- dropouten · CANONICAL
Relations from this entry
- cmrwhb9js006isoacsf207m10INSTANCE_OF →
dropout is a specific kind of regularization technique. A competent speaker would call dropout 'a regularization method.' It randomizes neuron deactivation to prevent co-adaptation, fitting squarely under the regularization umbrella.
- cmsllb41u07h9nobpx2ytm2wnDEPENDS_ON →
Removal test: remove neural network architecture and dropout stops operating — dropout is a technique that randomly deactivates neurons during training. Without a neural network architecture to apply it to, dropout has nothing to operate on.
- cmrwhb9js006isoacsf207m10SERVES →
Dropout is built and maintained for the sake of regularization: randomly dropping neurons during training reduces co-adaptation and prevents overfitting. Its designed purpose is to serve as a regularization mechanism.
- cmrg0pc1x00e82a1nppcnwovjSERVES →
dropout SERVES neural network training: dropout is maintained for the sake of improving neural network training. Its designed purpose is to reduce overfitting during training by randomly deactivating neurons, which serves the broader goal of producing better-trained neural networks. The servant (dropout) points at the master (neural network).
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Record identity
- Created
- Aug 9, 2026, 8:14 AM UTC
- Content hash
- 54203bb7af6d524f2328010fbdcc4fa187a2d64de13d14250e338a3d23af2fea