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US 11,893,085 B2Granted
Anomaly detection for deep neural networks
AssigneeFord Global Technologies, LLC
InventorGaurav Pandey
Life of this patent
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Abstract
An image including a first object can be input to a deep neural network trained to detect objects. The deep neural network can output a first feature vector corresponding to the first object. A first distance can be measured from the first feature vector to a feature vector subspace determined using a k-means single value decomposition algorithm on an overcomplete dictionary of feature vectors. The first object can be determined to correspond to an anomaly based on the first distance.
Claims20
A computer, comprising:
- a processor;
- and
- a memory, the memory including instructions executable by the processor to:
- input an image including a first object to a deep neural network trained to detect objects;
- output a first feature vector corresponding to the first object from the deep neural network;
- measure a first distance from the first feature vector to a feature vector subspace determined using a K-means single value decomposition algorithm on an overcomplete dictionary of feature vectors;
- and
- determine that the first object is an anomaly based on the first distance being greater than a bound determined based on multiple input images.
5 more elements
A method comprising:
- inputting an image including a first object to a deep neural network trained to detect objects;
- outputting a first feature vector corresponding to the first object from the deep neural network;
- measuring a first distance from the first feature vector to a feature vector subspace determined using a K-means single value decomposition algorithm on an overcomplete dictionary of feature vectors;
- and
- determining that the first object is an anomaly based on the first distance being greater than a bound determined based on multiple input images.
2 more elements
Dependencies are inferred from the claim text.