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

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

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

(e.g., Computer Science, Art History, or Forensics?)

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.

To investigate the representational value of specific data points within the broader training set. 2. Methodology

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