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• W2991167084 abstract "Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) have been observed to assign lower uncertainty to the OOD than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images. Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training. Our detector is simple, efficient at test time, and outperforms baseline OOD detectors in various domains. Further results show that SVD-RND learns better target distribution representation than the baseline RND algorithm. Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy on the CelebA dataset." @default.
• W2991167084 created "2019-12-05" @default.
• W2991167084 creator A5057794084 @default.
• W2991167084 creator A5085845584 @default.
• W2991167084 date "2019-11-27" @default.
• W2991167084 modified "2023-09-25" @default.
• W2991167084 title "Novelty Detection Via Blurring." @default.
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• W2991167084 hasPublicationYear "2019" @default.
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