# Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling Source: [Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:07:03.168Z Authors: Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah Published: 2018-07-02T18:12:32.000Z ## Abstract Deep Neural Networks (DNNs) have become increasingly popular in computer vision, natural language processing, and other areas. However, training and fine-tuning a deep learning model is computationally intensive and time-consuming. We propose a new method to improve the performance of nearly every model including pre-trained models. The proposed method uses an ensemble approach where the networks in the ensemble are constructed by reassigning model parameter values based on the probabilistic distribution of these parameters, calculated towards the end of the training process. For pre-trained models, this approach results in an additional training step (usually less than one epoch). We perform a variety of analysis using the MNIST dataset and validate the approach with a number of DNN models using pre-trained models on the ImageNet dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()