# Knee or ROC

Source: [Knee or ROC](<https://www.sota2.com/research/paper/68fa71d61e209131cea5345b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-26T05:00:04.121Z

Authors: Veronica Wendt, Jacob Steiner, Byunggu Yu, Caleb Kelly, Justin Kim

Published: 2024-01-14T23:25:44.000Z

## Abstract

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 threshold can address the instances of multiclass input images. However, this approach is unsuitable in instances where image population representation is unknown. We then 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 multiclass image detection.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.07390>)

[PDF](<https://arxiv.org/pdf/2401.07390.pdf>)