Summary of how individual data point audits can lead to more robust AI models.
To investigate the representational value of specific data points within the broader training set. 2. Methodology
(e.g., Computer Science, Art History, or Forensics?) 148_1000.jpg
1. Introduction
The rise of deep learning relies on massive datasets where individual image quality and annotation accuracy are often assumed rather than verified. Summary of how individual data point audits can
Testing how minor augmentations (rotations, color jitters) to this image change the model's confidence. 4. Conclusion
Recommendations for automated "cleaning" of datasets based on high-loss samples. 148_1000.jpg
Generating Grad-CAM visualizations to identify which pixels the model focuses on when classifying this specific image. 3. Results & Discussion