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What are label smoothing and mixup, and why do they help?

Two inexpensive regularizers that cure overconfident classifiers. What proves you understand them is knowing that one softens the target while the other softens the input, and precisely why gentler signals produce calibrated models. Here is the answer.

Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.

Two inexpensive regularizers that cure overconfident classifiers. What proves you understand them is knowing that one softens the target while the other softens the input, and precisely why gentler signals produce calibrated models. Here is the answer.

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