BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion

arXiv:2403.15766v1 Announce Type: new
Abstract: Bagging has achieved great success in the field of machine learning by integrating multiple base classifiers to build a single strong classifier to reduce model variance. The performance improvement of bagging mainly relies on the number and diversity of base classifiers. However, traditional deep learning model training methods are expensive to train individually and difficult to train multiple models with low similarity in a restricted dataset. Recently, diffusion models, which have been tremendously successful in …

abstract arxiv build classifier classifiers cs.ai cs.lg deep learning deep learning training diffusion diversity however improvement machine machine learning multiple network neural network performance reduce success training type variance