  Variational Auto-Encoders (VAEs) have shown great potential in the
unsupervised learning of data distributions. An VAE trained on normal images is
expected to only be able to reconstruct normal images, allowing the
localization of anomalous pixels in an image via manipulating information
within the VAE ELBO loss. The ELBO consists of KL divergence loss (image-wise)
and reconstruction loss (pixel-wise). It is natural and straightforward to use
the later as the predictor. However, usually local anomaly added to a normal
image can deteriorate the whole reconstructed image, causing segmentation using
only naive pixel errors not accurate. Energy based projection was proposed to
increase the reconstruction accuracy of normal regions/pixels, which achieved
the state-of-the-art localization accuracy on simple natural images. Another
possible predictors are ELBO and its components gradients with respect to each
pixels. Previous work claimed that KL gradient is a robust predictor. In this
paper, we argue that the energy based projection in medical imaging is not as
useful as on natural images. Moreover, we observe that the robustness of KL
gradient predictor totally depends on the setting of the VAE and dataset. We
also explored the effect of the weight of KL loss within beta-VAE and predictor
ensemble in anomaly localization.
