The rise of deep learning relies on massive datasets where individual image quality and annotation accuracy are often assumed rather than verified.
(e.g., An animal, a vehicle, a medical scan?)
Testing how minor augmentations (rotations, color jitters) to this image change the model's confidence. 4. Conclusion 148_1000.jpg
Generating Grad-CAM visualizations to identify which pixels the model focuses on when classifying this specific image. 3. Results & Discussion
Recommendations for automated "cleaning" of datasets based on high-loss samples. The rise of deep learning relies on massive
Summary of how individual data point audits can lead to more robust AI models.
Is 148_1000.jpg a prototypical example of its class, or is it an outlier? 148_1000.jpg
1. Introduction