Divide and Contrast: Learning Robust Temporal Features without Augmentation
We present Di-COT, a framework for unsupervised time-series representation learning. By contrasting sub-blocks within instances, it avoids data augmentation, achieving state-of-the-art accuracy on downstream tasks with much lower training time.