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Han Bao1

  • 1Institute of Statistical Mathematics, Kyoto 6068501, Japan bao.han@ism.ac.jp.

Neural computation
|August 14, 2025
PubMed
概括

特性正常化对于防止非对比学习的崩至关重要. 这项研究表明,与L2损失不同,共数损失诱导了稳定的动态,确保了没有负面示例的强有力的表示学习.

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