148_1000.jpg Instant

Edge cases or "noisy" samples (like 148_1000.jpg ) can disproportionately affect model convergence or bias.

Recommendations for automated "cleaning" of datasets based on high-loss samples.

1. Introduction

Summary of how individual data point audits can lead to more robust AI models.

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

Measuring the cross-entropy loss contribution of this single image during a training epoch. Edge cases or "noisy" samples (like 148_1000

(e.g., ImageNet, a local project, or a specific website?)