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• W2901197893 abstract "With the recent surge of interests in deep neural networks, more real-worldapplications start to adopt it in practice. However, deep neural networks areknown to have limited control over its prediction under unseen images. Suchweakness can potentially threaten society and cause annoying consequences inreal-world scenarios. In order to resolve such issue, a popular task calledout-of-distribution detection was proposed, which aims at separatingout-of-distribution images from in-distribution images. In this paper, wepropose a perturbed prior network architecture, which can efficiently separatemodel-level uncertainty from data-level uncertainty via prior entropy. Tofurther enhance the robustness of proposed entropy-based uncertainty measure,we propose a concentration perturbation algorithm, which adaptively adds noiseto concentration parameters so that the in- and out-of-distribution images arebetter separable. Our method can directly rely on the pre-trained deep neuralnetwork without re-training it, and also requires no knowledge about thenetwork architecture and out-of-distribution examples. Such simplicity makesour method more suitable for real-world AI applications. Through comprehensiveexperiments, our methods demonstrate its superiority by achievingstate-of-the-art results on many datasets." @default.
• W2901197893 created "2018-11-29" @default.
• W2901197893 creator A5050195037 @default.
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• W2901197893 date "2018-11-18" @default.
• W2901197893 modified "2023-09-24" @default.
• W2901197893 title "Enhancing the Robustness of Prior Network in Out-of-Distribution Detection." @default.
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• W2901197893 hasPublicationYear "2018" @default.
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