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A new Lossy Causal Temporal Convolutional Neural Network Autoencoder foranomaly detection is proposed in this work. Our framework uses arate-distortion loss and an entropy bottleneck to learn a compressed latentrepresentation for the task. The main idea of using a rate-distortion loss isto introduce representation flexibility that ignores or becomes robust tounlikely events with distinctive patterns, such as anomalies. These anomaliesmanifest as unique distortion features that can be accurately detected intesting conditions. This new architecture allows us to train a fullyunsupervised model that has high accuracy in detecting anomalies from adistortion score despite being trained with some portion of unlabelledanomalous data. This setting is in stark contrast to many of thestate-of-the-art unsupervised methodologies that require the model to be onlytrained on "normal data". We argue that this partially violates the concept ofunsupervised training for anomaly detection as the model uses an informeddecision that selects what is normal from abnormal for training. Additionally,there is evidence to suggest it also effects the models ability atgeneralisation. We demonstrate that models that succeed in the paradigm wherethey are only trained on normal data fail to be robust when anomalous data isinjected into the training. In contrast, our compression-based approachconverges to a robust representation that tolerates some anomalous distortion.The robust representation achieved by a model using a rate-distortion loss canbe used in a more realistic unsupervised anomaly detection scheme.

Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection