This paper proposes a novel loss concept for supervised classification tasks. Rather than enforcing a direct mapping from each input sample to a single assigned label, we define an optimization objective over all classifier outputs as a bimodal Gaussian distribution. This softer target formulation implicitly captures class ambiguity, mitigates overfitting, and encourages the learning of more robust decision boundaries, all without requiring additional label information. Experimental results demonstrate consistent improvements in robustness, with particularly pronounced gains in low-data regimes, while requiring only minimal modifications to standard training pipelines.

翻译：本文提出了一种用于监督分类任务的新型损失函数概念。不同于强制将每个输入样本直接映射到单一指定标签，我们定义了一个基于所有分类器输出的双模态高斯分布优化目标。这种更柔性的目标表达形式能够隐式捕捉类别模糊性、缓解过拟合，并促进学习更具鲁棒性的决策边界，且无需额外标签信息。实验结果表明，该方法在鲁棒性方面取得一致提升，在低数据场景下效果尤为显著，而对标准训练流程仅需极简修改。