148_1000.jpg Direct

Testing how minor augmentations (rotations, color jitters) to this image change the model's confidence. 4. Conclusion

The rise of deep learning relies on massive datasets where individual image quality and annotation accuracy are often assumed rather than verified. 148_1000.jpg

Applying t-SNE or UMAP to see where this image sits relative to its assigned class. Testing how minor augmentations (rotations

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

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

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