Knee or ROC
This addresses accuracy calculation challenges in multi-class image detection for scenarios with unknown population distributions, but it is incremental as it adapts existing methods.
The paper tackles the problem of multi-class image detection when image population representation is unknown, proposing the knee method for threshold determination and comparing it to ROC thresholds on a CIFAR-10 dataset.
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.