What semantics should be reflected in batching for identification?

Beddings for identification in machine learning tasks should reflect semantics that allow for unambiguous and efficient differentiation of objects or entities. This means that vector representations (embeddings) should:

• Capture key characteristics of the object that are important for the identification task.
• Ensure proximity in feature space for similar objects and distance for different ones.
• Be robust to noise and data variations.

For example, in face recognition tasks, embeddings should reflect unique facial features so that different people have different vectors, and different images of the same person are close. This allows the use of distances between embeddings for identification and verification.