Paul Honeine

Existing SF-UniDA methods fail catastrophically on time series due to a highly sensitive inference threshold, but a novel auto-thresholding module offers a universal fix.

Mixup-based distillation not only boosts student accuracy but also enhances uncertainty estimation, challenging the conventional view of knowledge transfer in deep learning.