Yinghua Shen

GECL: Fine-grained granular envelope contrastive learning for unsupervised domain adaptation.

This work proposes a novel Granular Envelope Contrastive Learning (GECL) method that explicitly models intra-class variations by generating multiple granular envelopes for each class, and jointly reduces domain discrepancy and enhances feature discriminability.