Divide and Contrast: Learning Robust Temporal Features without Augmentation

ArXi:2605.21241v1 Announce Type: new Self-supervised learning for time-series representation aims to reduce reliance on labeled data while maintaining strong downstream performance, yet many existing approaches incur high computational costs or rely on assumptions that do not hold across diverse temporal dynamics. In this work, we