Booth Id:
ROBO005T
Category:
Robotics and Intelligent Machines
Year:
2023
Finalist Names:
Ahiakpor, Priscilla (School: Lincoln Community School)
Gupta, Ridaan (School: Lincoln Community School)
Abstract:
Purpose
When artificial neural networks are deployed in real world situations, they often show flaws in their handling of data outside the training set. This kind of data is called out of distribution data (OOD). In some applications such as medical imaging or self-driving cars, this can pose danger as it may give high confidence prediction in situations they haven’t been properly trained to handle. Our research seeks to find new methods of distinguishing OOD samples so as to improve their reliability.
Method
Our method attempts to take advantage of clusters in feature space of the neural network to form boundaries that detect any OOD data that tries to cross and prevents them from crossing.
Results
The performance of this method was evaluated against a benchmark for OOD detection and it attained an accuracy of 90% across all experiments.
Conclusion
The results obtained from the tests proved that it was suitable for OOD detection. Future research may seek to explore this method further.