How do Mixture of Experts (MoE) architectures support unsupervised learning?

MoE models route inputs to specialized subnetworks, enabling scalable unsupervised representation learning.

Mixture of Experts architectures consist of multiple expert networks and a gating mechanism that routes each input to a subset of experts. In unsupervised settings, routing can be learned via clustering-like objectives or self-supervised signals. This allows specialization of experts for different data regions, improving efficiency and scalability in very large models.