What is cosine similarity loss in vision embedding learning?

Cosine similarity loss measures angular distance between embeddings instead of Euclidean distance.

In vision representation learning, direction of embeddings is often more important than magnitude. Cosine similarity loss normalizes vectors and optimizes angle alignment between predictions and targets. It is widely used in metric learning, contrastive learning, and face recognition systems.