Self-Distilled Representation Learning Versus Contrastive Approaches for Noisy Time-Series Classification Robustness

Abstract

Abstract: Self-supervised learning for time-series data holds potential similar to that recently unleashed in Natural Language Processing and Computer Vision. While most existing works in this area focus on contrastive learning, we propose a conceptually simple yet powerful non-contrastive approach, based on the data2vec self-distillation framework. The core of our method is a student-teacher scheme that predicts the latent representation of an input time series from masked views of the same time series. This strategy avoids strong modality-specific assumptions and biases typically introduced by the des

Research Question

Do self-distilled representation learning methods demonstrate superior robustness to input noise compared to contrastive learning approaches on standard time-series classification benchmarks?

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