Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2203.02194v5
• Date: Wed, 29 Mar 2023 02:13:23 GMT
• Title: Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection
• Authors: Yibo Zhou
• Abstract summary: Reconstruction autoencoder-based methods deal with the problem by using input reconstruction error as a metric of novelty vs. normality. We introduce semantic reconstruction, data certainty decomposition and normalized L2 distance to substantially improve original methods. Our method works without any additional data, hard-to-implement structure, time-consuming pipeline, and even harming the classification accuracy of known classes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In some scenarios, classifier requires detecting out-of-distribution samples far from its training data. With desirable characteristics, reconstruction autoencoder-based methods deal with this problem by using input reconstruction error as a metric of novelty vs. normality. We formulate the essence of such approach as a quadruplet domain translation with an intrinsic bias to only query for a proxy of conditional data uncertainty. Accordingly, an improvement direction is formalized as maximumly compressing the autoencoder's latent space while ensuring its reconstructive power for acting as a described domain translator. From it, strategies are introduced including semantic reconstruction, data certainty decomposition and normalized L2 distance to substantially improve original methods, which together establish state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method works without any additional data, hard-to-implement structure, time-consuming pipeline, and even harming the classification accuracy of known classes.

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