Leland Stanford Junior University

Ford Global Technolgoies, LLC

Eric Felix Darve

Daniel Ratner

Iman Soltani Bozchalooi

Ryan Humble

Ziyi Yang

Systems and method for detecting anomalies in accordance with embodiments of the invention are illustrated. One embodiment includes receiving a first data stream collected from a first sensor, identifying a first set of anomalies in the first data stream using a first model, receiving a second data stream collected from a second sensor, identifying a second set of anomalies in the second data stream using a second model, determining a set of joint anomalies using the first set of anomalies, second set of anomalies, and a threshold, wherein a threshold is some time period, and updating how anomalies are identified in the first and second set of models using the set of joint anomalies.

Systems and methods for anomaly detection in multi-modal data streams

Systems and methods for anomaly detection in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a system for detecting anomalous samples. The method draws data samples from a data distribution of true samples and an anomaly distribution and draws a latent sample from a latent space. The method further includes steps for training a generator to generate data samples based on the drawn data samples and the latent sample, and training a cyclic discriminator to distinguish between true data samples and reconstructed samples. A reconstructed sample is generated by the generator based on an encoding of a data sample. The method identifies a set of one or more true pairs, a set of one or more anomalous pairs, and a set of one or more generated pairs. The method trains a joint discriminator to distinguish true pairs from anomalous and generated pairs.

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Foster City, CA, United States of America

Eric Felix Darve