UNCERTAINTY-GUIDED CONTRASTIVE LEARNING FOR SINGLE SOURCE DOMAIN GENERALISATION

A new model is presented in the paper for single source domain generalisation, through augmentation of input and label spaces and using contrastive learning. Uncertainty estimation is also generated at inference time. Experimental results illustrate the improved performance produced by the presented approach.  © 2024 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. </p