  In this research, we present SLYKLatent, a novel approach for enhancing gaze
estimation by addressing appearance instability challenges in datasets due to
aleatoric uncertainties, covariant shifts, and test domain generalization.
SLYKLatent utilizes Self-Supervised Learning for initial training with facial
expression datasets, followed by refinement with a patch-based tri-branch
network and an inverse explained variance-weighted training loss function. Our
evaluation on benchmark datasets achieves an 8.7% improvement on Gaze360,
rivals top MPIIFaceGaze results, and leads on a subset of ETH-XGaze by 13%,
surpassing existing methods by significant margins. Adaptability tests on
RAF-DB and Affectnet show 86.4% and 60.9% accuracies, respectively. Ablation
studies confirm the effectiveness of SLYKLatent's novel components. This
approach has strong potential in human-robot interaction.
