Computer Science > Machine Learning
arXiv:2203.12145 (cs)
[Submitted on 23 Mar 2022 (v1), last revised 6 Sep 2022 (this version, v2)]
Title:Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem
View a PDF of the paper titled Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem, by Amir Emad Marvasti and 2 other authors
View PDFAbstract: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.
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2203.12145 [cs.LG] |
| (orarXiv:2203.12145v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2203.12145 arXiv-issued DOI via DataCite |
Submission history
From: Amir Emad Marvasti [view email][v1] Wed, 23 Mar 2022 02:42:08 UTC (767 KB)
[v2] Tue, 6 Sep 2022 19:24:28 UTC (832 KB)
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View a PDF of the paper titled Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem, by Amir Emad Marvasti and 2 other authors
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