Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection
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Use of deep generative models for unsupervised anomaly detection has shown great promise partially owing to their ability to learn proper representations of complex input data distributions. Current methods, however, lack a strong latent representation of the data, thereby resulting in sub-optimal unsupervised anomaly detection results. In this work, we propose a novel representation learning technique using deep autoencoders to tackle the problem of unsupervised anomaly detection. Our approach replaces the L_p reconstruction loss in the autoencoder optimization objective with a novel adversarial loss to enforce semantic-level reconstruction. In addition, we propose a novel simplex interpolation loss to improve the structure of the latent space representation in the autoencoder. Our technique improves the state-of-the-art unsupervised anomaly detection performance by a large margin on several image datasets including MNIST, fashion MNIST, CIFAR and Coil-100 as well as on several non-image datasets including KDD99, Arrhythmia and Thyroid. For example, On the CIFAR-10 dataset, using a standard leave-one-out evaluation protocol, our method achieves a substantial performance gain of 0.23 AUC points compared to the state-of-the-art.
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