Turn Patch Embeddings into a Bias and Robustness Detector
May 22, 2025
When you analyze which patches are semantically or visually similar, you're essentially exploring how the model "understands" the visual world. This can be very useful for detecting bias in data/AI.
Interpretability
If certain patches are highly similar in embedding space - even across different images - it means the model sees them as having similar content (e.g., texture, shape, edges, or semantics).
By analyzing these embeddings, we can understand what the model is actually focusing on. For example, if a chest X-ray model consistently groups patches with lung edges - regardless of pathology - it might be over-relying on anatomical boundaries rather than disease cues. This insight can guide fine-tuning to shift focus toward clinically relevant regions.
Detecting Failure Modes
If you see irrelevant patches (e.g., background or artifacts) clustering with important ones, that could explain why your model makes wrong predictions.
Helps in debugging models.
Understanding Feature Learning
You can track how patch representations evolve across layers or training epochs:
- Early layers: similar color or texture
- Later layers: semantic meaning
This reveals how abstract the learned features are.
Data Quality Assessment
If similar-looking patches have very different embeddings, it might mean:
- Model is not robust
- Data is noisy or inconsistent
If dissimilar patches have similar embeddings, it could indicate representation collapse.
Self-supervised Learning Evaluation
- In contrastive learning and masked image modeling (e.g., MAE, DINO), learning is successful when semantically similar patches are embedded close together in representation space. However, when a dataset contains a high density of visually similar patches, the model may overfit to redundant features. In the worst case, this can amplify hidden biases by reinforcing dominant patterns and ignoring minority signals.