Case-Study: A Generative Approach to Anomaly Detection

Problem

Our client was an SME renowned for its solutions validating the ingenuity of user-supplied data. They were interested in anomaly detection for various reasons, including the potential to detect data drifting.

Solution

We provided an imaging-based anomaly detection solution that combined the capacity to model (“understand” if you wish) the underlying distribution as well as detect discrepancies relative to it. As we embarked on this mission, we extended our usual toolset of variational and adversarial auto-encoders, for the first time with our custom diffusion models.

The resulting 100% custom codebase was developed mostly in PyTorch.

Results

The client was happy with the expansion of their existing toolbox and decided to integrate it into their own toolkit. Their team took over our PoC for further productization.

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