Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
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发表时间：
2018-07
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通讯作者：
Stanislav Pidhorskyi;Ranya Almohsen;D. Adjeroh;Gianfranco Doretto
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作者：
Stanislav Pidhorskyi;Ranya Almohsen;D. Adjeroh;Gianfranco Doretto
新奇检测是识别新数据点被认为是内点还是离群点的问题。我们假设训练数据仅可用于描述内点分布。最近的方法主要利用深度编码器-解码器网络架构来计算重建误差，该重建误差用于计算新奇分数或训练一类分类器。虽然我们也利用了这种新型网络，但我们采用了概率方法，并有效地计算了样本由内点分布生成的可能性。我们通过两个主要贡献来实现这一目标。首先，我们使计算的新奇概率可行，因为我们线性化的参数化流形捕捉底层结构的内点分布，并显示如何分解的概率，可以计算相对于局部坐标的流形切空间。其次，我们改进了自编码器网络的训练。一组广泛的结果表明，该方法在几个基准数据集上实现了最先进的性能。
Novelty detection is the problem of identifying whether a new data point is considered to be an inlier or an outlier. We assume that training data is available to describe only the inlier distribution. Recent approaches primarily leverage deep encoder-decoder network architectures to compute a reconstruction error that is used to either compute a novelty score or to train a one-class classifier. While we too leverage a novel network of that kind, we take a probabilistic approach and effectively compute how likely it is that a sample was generated by the inlier distribution. We achieve this with two main contributions. First, we make the computation of the novelty probability feasible because we linearize the parameterized manifold capturing the underlying structure of the inlier distribution, and show how the probability factorizes and can be computed with respect to local coordinates of the manifold tangent space. Second, we improve the training of the autoencoder network. An extensive set of results show that the approach achieves state-of-the-art performance on several benchmark datasets.