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.
@article{arxiv.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}
}
Comments
Accepted by the AAAI-25 Workshop on Artificial Intelligence for Time Series Analysis (AI4TS)