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Overfitting is a major problem in training machine learning models,specifically deep neural networks. This problem may be caused by imbalanceddatasets and initialization of the model parameters, which conforms the modeltoo closely to the training data and negatively affects the generalizationperformance of the model for unseen data. The original dropout is aregularization technique to drop hidden units randomly during training. In thispaper, we propose an adaptive technique to wisely drop the visible and hiddenunits in a deep neural network using Ising energy of the network. Thepreliminary results show that the proposed approach can keep the classificationperformance competitive to the original network while eliminating optimizationof unnecessary network parameters in each training cycle. The dropout state ofunits can also be applied to the trained (inference) model. This techniquecould compress the network in terms of number of parameters up to 41.18% and55.86% for the classification task on the MNIST and Fashion-MNIST datasets,respectively.

Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks