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为了增强分布外泛化能力，学者们可以生成多样化的数据，模拟不同的测试环境，还可以通过域适应技术（Domain Adaptation）<ref>Stan S ,Rostami M . Source-free domain adaptation for semantic image segmentation using internal representations [J]. Frontiers in Big Data, 2024, 7 1359317-1359317.</ref>，使模型可以适应不同的测试数据分布。另外，学者们也提出了[[不变性学习]]（Invariant Learning）<ref>L G M ,S A D ,M C S . Variability in training unlocks generalization in visual perceptual learning through invariant representations. [J]. Current biology : CB, 2023, 33 (5): 817-826.e3.</ref>、[[元学习]]（Meta Learning）<ref>Zhang B ,Gao B ,Liang S , et al. A classification algorithm based on improved meta learning and transfer learning for few‐shot medical images [J]. IET Image Processing, 2023, 17 (12): 3589-3598.</ref>等方法解决该问题。

为了增强分布外泛化能力，学者们可以生成多样化的数据，模拟不同的测试环境，还可以通过域适应技术（Domain Adaptation）<ref>Stan S ,Rostami M . Source-free domain adaptation for semantic image segmentation using internal representations [J]. Frontiers in Big Data, 2024, 7 1359317-1359317.</ref>，使模型可以适应不同的测试数据分布。另外，学者们也提出了[[不变性学习]]（Invariant Learning）<ref>L G M ,S A D ,M C S . Variability in training unlocks generalization in visual perceptual learning through invariant representations. [J]. Current biology : CB, 2023, 33 (5): 817-826.e3.</ref>、[[元学习]]（Meta Learning）<ref>Zhang B ,Gao B ,Liang S , et al. A classification algorithm based on improved meta learning and transfer learning for few‐shot medical images [J]. IET Image Processing, 2023, 17 (12): 3589-3598.</ref>等方法解决该问题。

= 神经信息压缩机（NIS）介绍 =

= 机器学习识别因果涌现问题 =

== 数学问题定义 ==

== 数学问题定义 ==