Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations

Understand

Learning visual concepts from raw images without strong supervision is a challenging task.

• In this work, we show the advantages of prototype representations for understanding and revising the latent space of neural concept learners.
• For this purpose, we introduce interactive Concept Swapping Networks (iCSNs), a novel framework for learning concept-grounded representations via weak supervision and implicit prototype representations.
• iCSNs learn to bind conceptual information to specific prototype slots by swapping the latent representations of paired images.