Systems and methods for out-of-distribution detection

A system for out-of-distribution (OOD) anomaly detection of images for an autonomous driving system, the system comprising:

• a memory storing instructions for OOD anomaly detection of test data;
• and
• a processor configured to read and execute the instructions from the memory to:
• implement a neural network composed of a plurality of layers on the processor as a traffic sign classification model for the autonomous driving system;
• receive, for the neural network, a set of training images containing traffic signs and other items generated according to a preset distribution of the set of training images, wherein each layer of the plurality of layers of the neural network transforms a respective input to a respective output via a respective mapping function of a plurality of mapping functions;
• train the neural network using the set of training images, to classify an incoming image depicting a traffic scene as an anomaly by determining whether the incoming image is OOD, wherein the training includes:
• applying one or more geometric transformations including vertical and/or horizontal translation of a specific number of pixels to each training image of the set of the training images to create a set of transformed training images;
• generating, by the plurality of layers of the neural network implemented on the processor, a feature map by combining mapping functions of the plurality of layers into a vector of mapping function elements that comprises:
• a first mapping function element of a first mapping function from the plurality of mapping functions, and a second mapping function element of a function composition of the first mapping function and a second mapping function from the plurality of mapping functions;
• mapping, by the feature map generated by the plurality of layers of the neural network, the set of transformed training images to a set of feature space training data in a feature space;
• and
• identifying, via the processor, a hyper-ellipsoid in the feature space enclosing the set of feature space training data based on the generated feature map as a mechanism for determining whether the incoming image is OOD;
• receive, at inference time of the neural network associated with the identified hyper-ellipsoid used as the traffic sign classification model in the autonomous driving system, a first test image depicting a real world scene outside the set of training images;
• apply, via the processor, one or more geometric transformations including vertical and/or horizontal translation of a specific number of pixels to the first test image to create one or more transformed test images;
• use the trained neural network associated with the identified hyper-ellipsoid to determine whether the first test image is OOD, including:
• mapping the one or more transformed test images into the feature space by the generated feature map;
• generating, via the processor, a prediction indicating that the first test image is OOD based on whether the mapped one or more transformed test images in the feature space are outside the hyper-ellipsoid identified from the training of the neural network;
• and
• responsive to determining that the first test image is OOD, generate, by the trained neural network associated with the identified hyper-ellipsoid implemented as the traffic sign classification model in the autonomous driving system, a traffic sign classification output indicating that the first test image depicting the real world scene is an anomaly.

A non-transitory computer-readable medium (CRM) having stored thereon machine-readable instructions for out-of-distribution (OOD) anomaly detection of images for an autonomous driving system, the machine-readable instructions executable to cause a processor to perform operations comprising:

• implementing a neural network composed of a plurality of layers on the processor as a traffic sign classification model for the autonomous driving system;
• receiving, for the neural network, a set of training images containing traffic signs and other items generated according to a preset distribution of the set of training images, wherein each layer of the plurality of layers of the neural network transforms a respective input to a respective output via a respective mapping function of a plurality of mapping functions;
• training the neural network using the set of training images, to classify an incoming image depicting a traffic scene as an anomaly by determining whether the incoming image is OOD, wherein the training includes:
• applying one or more geometric transformations including vertical and/or horizontal translation of a specific number of pixels to each training image of the set of the training images to create a set of transformed training images;
• generating, by the plurality of layers of the neural network implemented on the processor, a feature map by combining mapping functions of the plurality of layers into a vector of mapping function elements that comprises:
• a first mapping function element of a first mapping function from the plurality of mapping functions, and a second mapping function element of a function composition of the first mapping function and a second mapping function from the plurality of mapping functions;
• mapping, by the feature map generated by the plurality of layers of the neural network, the set of transformed training images to a set of feature space training data in a feature space;
• and
• identifying, via the processor, a hyper-ellipsoid in the feature space enclosing the set of feature space training data based on the generated feature map as a mechanism for determining whether the incoming image is OOD;
• receiving, at inference time of the neural network associated with the identified hyper-ellipsoid used as the traffic sign classification model in the autonomous driving system, a first test image depicting a real world scene outside the set of training images;
• applying, via the processor, one or more geometric transformations including vertical and/or horizontal translation of a specific number of pixels to the first test image to create one or more transformed test images;
• using the trained neural network associated with the identified hyper-ellipsoid to determine whether the first test image is OOD, including:
• mapping the one or more transformed test images into the feature space by the generated feature map;
• generating, via the processor, a prediction indicating that the first test image is OOD based on whether the mapped one or more transformed test images in the feature space are outside the hyper-ellipsoid identified from the training of the neural network;
• and
• responsive to determining that the first test image is OOD, generating, by the trained neural network associated with the identified hyper-ellipsoid implemented as the traffic sign classification model in the autonomous driving system, a traffic sign classification output indicating that the first test image depicting the real world scene is an anomaly.