“Detecting Data Drift in Image Classification Neural Networks,” a Presentation from Southern Illinois University Carbondale

Spyros Tragoudas, Professor and School Director at Southern Illinois University Carbondale, presents the “Detecting Data Drift in Image Classification Neural Networks” tutorial at the May 2023 Embedded Vision Summit.

An unforeseen change in the input data is called “drift,” and may impact the accuracy of machine learning models. In this talk, Tragoudas presents a novel scheme for diagnosing data drift in the input streams of image classification neural networks. His proposed method for drift detection and quantification uses a threshold dictionary for the prediction probabilities of each class in the neural network model.

The method is applicable to any drift type in images such as noise and weather effects, among others. Tragoudas shares experimental results on various data sets, drift types and neural network models to show that his proposed method estimates the drift magnitude with high accuracy, especially when the level of drift significantly impacts the model’s performance.

See here for a PDF of the slides.

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