Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem
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Maximum Probability Framework, powered by Maximum Probability Theorem, is a recent theoretical development, 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 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 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 MPT based regularization strategy both stabilizes and improves the generalization and robustness of base models in addition to improved OOD performance on CIFAR10, CIFAR100 and MNIST datasets.
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