We formulate the fundamental laws governing learning dynamics, namely the conservation law and the decrease of total entropy. Within this framework, we introduce an entropy-based lifelong ensemble learning method. We evaluate its effectiveness by constructing an immunization mechanism to defend against transfer-based adversarial attacks on the CIFAR-10 dataset. Compared with a naive ensemble formed by simply averaging models specialized on clean and adversarial samples, the resulting logifold achieves higher accuracy in most test cases, with particularly large gains under strong perturbations.

翻译：我们阐述了支配学习动力学的基本定律，即守恒定律与总熵递减定律。在此框架下，我们提出了一种基于熵的终身集成学习方法。通过在CIFAR-10数据集上构建免疫机制以防御基于迁移的对抗攻击，我们评估了该方法的有效性。与简单平均干净样本和对抗样本专用模型所构建的朴素集成相比，所得的对数流形在多数测试案例中实现了更高的准确率，且在强扰动条件下提升尤为显著。