Intraclass clustering: an implicit learning ability that regularizes DNNs

• URL: http://arxiv.org/abs/2103.06733v1
• Date: Thu, 11 Mar 2021 15:26:27 GMT
• Title: Intraclass clustering: an implicit learning ability that regularizes DNNs
• Authors: Carbonnelle Simon and Christophe De Vleeschouwer
• Abstract summary: We show that deep neural networks are regularized through their ability to extract meaningful clusters among a class. Measures of intraclass clustering are designed based on the neuron- and layer-level representations of the training data.
• Score: 22.732204569029648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several works have shown that the regularization mechanisms underlying deep neural networks' generalization performances are still poorly understood. In this paper, we hypothesize that deep neural networks are regularized through their ability to extract meaningful clusters among the samples of a class. This constitutes an implicit form of regularization, as no explicit training mechanisms or supervision target such behaviour. To support our hypothesis, we design four different measures of intraclass clustering, based on the neuron- and layer-level representations of the training data. We then show that these measures constitute accurate predictors of generalization performance across variations of a large set of hyperparameters (learning rate, batch size, optimizer, weight decay, dropout rate, data augmentation, network depth and width).

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