148_1000.jpg -

Generating Grad-CAM visualizations to identify which pixels the model focuses on when classifying this specific image. 3. Results & Discussion

(e.g., An animal, a vehicle, a medical scan?) 148_1000.jpg

The rise of deep learning relies on massive datasets where individual image quality and annotation accuracy are often assumed rather than verified. a local project

(e.g., ImageNet, a local project, or a specific website?) 148_1000.jpg

Applying t-SNE or UMAP to see where this image sits relative to its assigned class.

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

Measuring the cross-entropy loss contribution of this single image during a training epoch.