Misclassified Image

Misclassified images in deep learning highlight the limitations of current models and datasets, prompting research into understanding and mitigating these errors. Current efforts focus on identifying spurious features (artifacts misleading classifiers) within datasets like ImageNet, developing methods to generate and detect these features using techniques like metamorphic testing and explainable AI (XAI), and analyzing the discrepancies between human and machine-generated counterfactual explanations for misclassifications. This research is crucial for improving model robustness, generalization, and ultimately, the reliability of image classification systems across various applications.