Perceptual Image Anomaly Detectionстатья

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Дата последнего поиска статьи во внешних источниках: 19 августа 2020 г.

Работа с статьей

[1] Perceptual image anomaly detection / N. Tuluptceva, B. Bakker, I. Fedulova, A. Konushin // Palaiahnakote S., Sanniti di Baja G., Wang L., Yan W. (eds) Pattern Recognition. ACPR 2019. — Vol. 12046 of Lecture Notes in Computer Science. — Springer, 2019. — P. 164–178. We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of normal data, aim to detect out-of-distribution (abnormal) samples. Our approach includes a combination of encoder and generator for mapping an image distribution to a predefined latent distribution and vice versa. It leverages Generative Adversarial Networks to learn these data distributions and uses perceptual loss for the detection of image abnormality. To accomplish this goal, we introduce a new similarity metric, which expresses the perceived similarity between images and is robust to changes in image contrast. Secondly, we introduce a novel approach for the selection of weights of a multi-objective loss function (image reconstruction and distribution mapping) in the absence of a validation dataset for hyperparameter tuning. After training, our model measures the abnormality of the input image as the perceptual dissimilarity between it and the closest generated image of the modeled data distribution. The proposed approach is extensively evaluated on several publicly available image benchmarks and achieves state-of-the-art performance. [ DOI ]

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