Also known as: Co adaptation
Co-adaptation is an undesirable training behavior in which neurons or model components rely too heavily on specific other components rather than learning robust representations. It can contribute to overfitting, and techniques such as dropout can reduce this dependency.
An undesirable behavior in which neurons predict patterns in training data by relying almost exclusively on outputs of specific other neurons instead of relying on the network's behavior as a whole. When the patterns that cause co-adaptation are not present in validation data, then co-adaptation causes overfitting. Dropout regularization reduces co-adaptation because dropout ensures neurons cannot rely solely on specific other neurons.