Self-supervised learning with swarm intelligence combines principles of self-supervised learning with techniques inspired by collective behavior patterns in swarms.
In such systems, multiple agents or models work together in a decentralized manner to recognize latent structures in data through mutual information exchange and coordination.
The agents independently generate pseudo-labels or tasks that they use mutually to improve their representations.
Typical mechanisms include communication, consensus building, and collective decision-making processes that create a robust and adaptive learning environment.
These approaches are especially useful when data is distributed or heterogeneous and central labels are missing.