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1. Introduction

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

Generating Grad-CAM visualizations to identify which pixels the model focuses on when classifying this specific image. 3. Results & Discussion 148_1000.jpg

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

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

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