What Are Class Outliers?
Class outliers are images that are technically assigned a valid label but don’t visually align with the common patterns for that label. They typically fall into one of two categories:- Unusual visual appearance Labeled correctly, but the image looks very different from others in the same class Example: A drawing of a dog in a dataset of real dog photographs
- No matching class exists The image visually belongs to a different category, even though its label is technically valid Example: A photo of a sheep labeled as “dog” in a dataset that only includes “dog” and “cat” classes
Examples of Class Outliers
Examples of Class Outliers
- A drawing of a dog in a dataset of real dog photos
- A sheep labeled as “dog” in a dataset with only “dog” and “cat” classes
Why It Matters
Need | Description |
---|---|
Model confusion | Outliers within a class can distort the learned distribution |
Cleaner training | Identifying and optionally excluding such samples can improve performance |
Dataset debugging | Helps uncover unexpected edge cases or data drift |
How to Detect
- Navigate to Add Filter → Class Outlier.
- Set the confidence threshold (default set to 1).
- Export results using Export → Matching the applied filter.