Editable Concept Bottleneck Models

Understand

Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a humanunderstandable concept layer.

• However, most previous studies focused on cases where the data, including concepts, are clean.
• In many scenarios, we often need to remove/insert some training data or new concepts from trained CBMs for reasons such as privacy concerns, data mislabelling, spurious concepts, and concept annotation errors.
• Thus, deriving efficient editable CBMs without retraining from scratch remains a challenge, particularly in large-scale applications.