Definition
Multiple Instance Learning (MIL) addresses scenarios where data is grouped into ‘bags’ with a single label, while individual instances within those bags remain unlabeled. A bag is typically positive if at least one instance is positive, and negative only if all instances are negative. This technique is crucial when precise labeling of individual data points is costly or impossible, allowing models to learn from coarse-grained supervision signals effectively.
Summary
A weakly supervised learning paradigm where labels are assigned to bags of instances rather than individual instances.
Key Concepts
- Bag-level labeling
- Instance-level uncertainty
- Weak supervision
- Positive/negative bag logic
Use Cases
- Drug activity prediction
- Image classification with bounding boxes
- Content-based image retrieval