Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
This work addresses data accountability for the Mars Science Laboratory ground data system, but appears incremental as it applies an existing optimization method to tune an existing model.
The paper tackled the problem of detecting missing data patches in Mars Curiosity rover transmissions by tuning a variational autoencoder with a derivative-free optimization method, achieving unspecified anomaly detection performance.
The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents $Δ$-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.