论文标题
具有最大概率定理的分布检测，概括和鲁棒性三角形
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem
论文作者
论文摘要
由最大概率定理提供支持的最大概率框架是人工智能的最新理论发展，旨在正式定义概率模型，指导目标函数的发展以及概率模型的正则化。 MPT使用模型在随机变量上假设的概率分布来提供模型概率的上限。我们通过将MPT作为CNN及其基于能量的变体的培训中的正规化方案纳入计算机视觉中的挑战（OOD）检测问题。我们证明了所提出的方法对1080个训练有素的模型的有效性（具有不同的超参数），得出的结论是，基于MPT的正则化策略稳定并提高了基本模型的概括和鲁棒性，以及在CIFAR10，CIFAR100，CIFAR100和MNIST数据集中增强了OOD性能。
Maximum Probability Framework, powered by Maximum Probability Theorem, is a recent theoretical development in artificial intelligence, aiming to formally define probabilistic models, guiding development of objective functions, and regularization of probabilistic models. MPT uses the probability distribution that the models assume on random variables to provide an upper bound on the probability of the model. We apply MPT to challenging out-of-distribution (OOD) detection problems in computer vision by incorporating MPT as a regularization scheme in the training of CNNs and their energy-based variants. We demonstrate the effectiveness of the proposed method on 1080 trained models, with varying hyperparameters, and conclude that the MPT-based regularization strategy stabilizes and improves the generalization and robustness of base models in addition to enhanced OOD performance on CIFAR10, CIFAR100, and MNIST datasets.
