The Sharpness-Aware Minimization (SAM) optimizer connects flatness and generalization, suggesting that loss basins with lower sharpness are correlated with better generalization. However, SAM requires manually tuning the open ball radius, which complicates its practical application. To address this, we propose a method inspired by linear connectivity, using two models initialized differently as endpoints to automatically determine the optimal open ball radius. Specifically, we introduce distance regularization between the two models during training, which encourages them to approach each other, thus dynamically adjusting the open ball radius. We design an optimization algorithm called ’Twin Stars Entwined’ (TSE), where the stopping condition is defined by the models’ linear connectivity, i.e., when they converge to a region of sufficiently low distance. As the models iteratively reduce their distance, they converge to a flatter region of the loss landscape. Our approach complements SAM by dynamically identifying flatter regions and exploring the geometric properties of multiple connected loss basins. Instead of searching for a single large-radius basin, we identify a group of connected basins as potential optimization targets. Experiments conducted across multiple models and in varied noise environments showed that our method achieved a performance on par with state-of-the-art techniques.

This paper proposes an Online Prototype Angular Balanced Self-Distillation (OPAB) framework to address the challenges posed by non-ideal annotation in remote sensing image semantic segmentation. “Non-ideal annotation” typically refers to scenarios where long-tailed class distributions and label noise coexist in both training and testing sets. Existing methods often tackle these two issues separately, overlooking the conflict between noisy samples and minority classes as well as the unreliable early stopping caused by non-clean validation sets, which exacerbates the model’s tendency to memorize noisy samples. OPAB mitigates the imbalance problem by employing an improved bilateral-branch network (BBN) that integrates max-min angular regularization (MMA) and category-level inverse weighting to achieve balanced hyperspherical representations. The balanced hyperspherical representations further facilitate noise-clean sample separation and early stopping estimation based on large category-wise Local Intrinsic Dimensionality (LID). Moreover, OPAB introduces a bootstrap teacher label refinement strategy coupled with a student full-parameter retraining mechanism to avoid memorizing noisy samples. Experimental results on ISPRS datasets demonstrate that OPAB achieves a 2.0% mIoU improvement under non-ideal annotation conditions and achieves 89% mIoU after cross-set correction, showcasing strong robustness across different backbones and effective iterative calibration capability.