How do you choose loss functions for computer vision tasks (classification, detection, segmentation)?
Cross-entropy is where you start, not where you finish. The signal is fitting the loss to the task structure: focal for detection's background flood, the IoU family for box overlap, Dice for imbalanced masks. Here is how to reason about it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Cross-entropy is where you start, not where you finish. The signal is fitting the loss to the task structure: focal for detection's background flood, the IoU family for box overlap, Dice for imbalanced masks. Here is how to reason about it.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
