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

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

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

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

Using a pre-trained ResNet-50 or Vision Transformer (ViT) to extract the embedding vector for 148_1000.jpg .

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

Is 148_1000.jpg a prototypical example of its class, or is it an outlier?