Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
Abstract
There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets that may not be well modeled in simulation. Therefore, relying on simulations for training will lead to sub-optimal performance in data, but the lack of true class labels makes it difficult to train on real data. To address this challenge we introduce a new approach, called Tag N' Train (TNT), that can be applied to unlabeled data that has two distinct sub-objects. The technique uses a weak classifier for one of the objects to tag signal-rich and background-rich samples. These samples are then used to train a stronger classifier for the other object. We demonstrate the power of this method by applying it to a dijet resonance search. By starting with autoencoders trained directly on data as the weak classifiers, we use TNT to train substantially improved classifiers. We show that Tag N' Train can be a powerful tool in model-agnostic searches and discuss other potential applications.
Keywords
Cite
@article{arxiv.2002.12376,
title = {Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data},
author = {Oz Amram and Cristina Mantilla Suarez},
journal= {arXiv preprint arXiv:2002.12376},
year = {2021}
}
Comments
20 pages, 7 figures. Updated to match version accepted in JHEP. Contains new background estimation method for TNT on non-resonant signals, more detailed significance comparisons between methods and an appendix illustrating why repeatedly training without using TNT does not work