Efficient remedies for outlier detection with variational autoencoders

Deep networks often make confident, yet incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that such likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest class of deep generative models. First, we show that a theoretically-grounded correction readily ameliorates a key bias with VAE likelihood estimates. The bias correction is model-free, sample-specific, and accurately computed with the Bernoulli and continuous Bernoulli visible distributions. Second, we show that a well-known preprocessing technique, contrast normalization, extends the effectiveness of bias correction to natural image datasets. Third, we show that the variance of the likelihoods computed over an ensemble of VAEs also enables robust outlier detection. We perform a comprehensive evaluation of our remedies with nine (grayscale and natural) image datasets, and demonstrate significant advantages, in terms of both speed and accuracy, over four other state-of-the-art methods. Our lightweight remedies are biologically inspired and may serve to achieve efficient outlier detection with many types of deep generative models.