The performance of image classifiers often hinges on their ability to correctly identify images across a wide range of scenarios, including those that may not have been encountered during training. In this paper, Jan proposes a novel approach to identify systematic errors in image classifiers, recognizing the importance of ensuring these models perform well even on rare corner cases. “If you have an image classifier trained on some data distribution, often in the long tail of this data distribution, there are cases which are not covered in the training or test data,” he points out. “In this case, the system could misclassify the images, which goes unnoticed because these cases are also not covered in the validation set.” 22 DAILY ICCV Wednesday Poster Presentation Identification of Systematic Errors of Image Classifiers on Rare Subgroups Jan Hendrik Metzen is a senior expert at the Bosch Center for Artificial Intelligence. His paper on auditing image classifiers to identify systematic errors has been accepted as a poster. He speaks to us ahead of his presentation this morning.
RkJQdWJsaXNoZXIy NTc3NzU=