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Deep Neural Networks (DNNs) have become increasingly popular in computervision, natural language processing, and other areas. However, training andfine-tuning a deep learning model is computationally intensive andtime-consuming. We propose a new method to improve the performance of nearlyevery model including pre-trained models. The proposed method uses an ensembleapproach where the networks in the ensemble are constructed by reassigningmodel parameter values based on the probabilistic distribution of theseparameters, calculated towards the end of the training process. For pre-trainedmodels, this approach results in an additional training step (usually less thanone epoch). We perform a variety of analysis using the MNIST dataset andvalidate the approach with a number of DNN models using pre-trained models onthe ImageNet dataset.

Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling