Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adversarial attacks, emphasizing the need for resilient methods that ensure robust performance and efficient model selection. We introduce ReLATE, a framework that identifies robust learners based on dataset similarity, reduces computational overhead, and enhances resilience. ReLATE maintains multiple deep learning models in well-known adversarial attack scenarios, capturing model performance. ReLATE identifies the most analogous dataset to a given target using a similarity metric, then applies the optimal model from the most similar dataset. ReLATE reduces computational overhead by an average of 81.2%, enhancing adversarial resilience and streamlining robust model selection, all without sacrificing performance, within 4.2% of Oracle.
View on arXiv@article{kocal2025_2503.07882,
title={ ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks },
author={ Cagla Ipek Kocal and Onat Gungor and Aaron Tartz and Tajana Rosing and Baris Aksanli },
journal={arXiv preprint arXiv:2503.07882},
year={ 2025 }
}