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An approach to utilize recent advances in deep generative models for anomalydetection in a granular (continuous) sense on a real-world image dataset withquality issues is detailed using recent normalizing flow models, withimplications in many other applications/domains/data types. The approach iscompletely unsupervised (no annotations available) but qualitatively shown toprovide accurate semantic labeling for images via heatmaps of the scaledlog-likelihood overlaid on the images. When sorted based on the median valuesper image, clear trends in quality are observed. Furthermore, downstreamclassification is shown to be possible and effective via a weakly supervisedapproach using the log-likelihood output from a normalizing flow model as atraining signal for a feature-extracting convolutional neural network. Thepre-linear dense layer outputs on the CNN are shown to disentangle high levelrepresentations and efficiently cluster various quality issues. Thus, anentirely non-annotated (fully unsupervised) approach is shown possible foraccurate estimation and classification of quality issues..

Granular Learning with Deep Generative Models using Highly Contaminated Data