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.
Applying t-SNE or UMAP to see where this image sits relative to its assigned class. 148_1000.jpg
Using a pre-trained ResNet-50 or Vision Transformer (ViT) to extract the embedding vector for 148_1000.jpg .
Is 148_1000.jpg a prototypical example of its class, or is it an outlier? Testing how minor augmentations (rotations
(e.g., Computer Science, Art History, or Forensics?)
Edge cases or "noisy" samples (like 148_1000.jpg ) can disproportionately affect model convergence or bias. or is it an outlier? (e.g.
(e.g., An animal, a vehicle, a medical scan?)