What is memory-augmented unsupervised learning?

It enhances models with external memory banks to store and retrieve representations over time.

Memory-augmented architectures store embeddings or latent representations outside the model parameters. This allows models to learn long-term dependencies and improve contrastive learning via large negative sample pools. Methods like MoCo use momentum-updated memory banks, while neural Turing machines and differentiable memory networks extend this concept further for unsupervised representation learning.