Model ensemble techniques often increase task performance in neural networks; however, they require increased time, memory, and management effort. In this study, we propose a novel method that replicates the effects of a model ensemble with a single model. Our approach creates K-virtual models within a single parameter space using K-distinct pseudo-tags and K-distinct vectors. Experiments on text classification and sequence labeling tasks on several datasets demonstrate that our method emulates or outperforms a traditional model ensemble with 1/K-times fewer parameters.
Cite
@article{arxiv.2005.00879,
title = {Single Model Ensemble using Pseudo-Tags and Distinct Vectors},
author = {Ryosuke Kuwabara and Jun Suzuki and Hideki Nakayama},
journal= {arXiv preprint arXiv:2005.00879},
year = {2020}
}