Knee or ROC
Veronica Wendt Byunggu Yu Caleb Kelly Junwhan Kim
Self-attention transformers have demonstrated accuracy for image
classification with smaller data sets. However, a limitation is that tests
to-date are based upon single class image detection with known representation
of image populations. For instances where the input image classes may be
greater than one and test sets that lack full information on representation of
image populations, accuracy calculations must adapt. The Receiver Operating
Characteristic (ROC) accuracy thresh-old can address the instances of
multi-class input images. However, this approach is unsuitable in instances
where image population representation is unknown. We consider calculating
accuracy using the knee method to determine threshold values on an ad-hoc
basis. Results of ROC curve and knee thresholds for a multi-class data set,
created from CIFAR-10 images, are discussed for multi-class image detection.