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随机性规范化与神经网络的简单一致性训练
IEEE transactions on pattern analysis and machine intelligence
|February 29, 2024
概括
R-Drop是一种新的一致性训练策略,可以减少神经网络训练中因脱落随机性引起的不一致性. 这种方法通过确保在训练和推理过程中提供一致的输出,提高了各种任务和网络类型的模型性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 随机性,如掉队,通常用于神经网络训练,以帮助优化和防止过度拟合.
- 然而,退出引入了培训和推理阶段之间的不一致性,可能会影响模型可靠性.
研究的目的:
- 引入R-Drop,一个简单的一致性训练策略,旨在规范神经网络中的随机性.
- 解决基于脱学的规范化方法所造成的培训推理不一致问题.
主要方法:
- 对于每个训练样本,R-Drop 通过dropout 基于随机抽取的两个输出分布之间强制执行一致性.
- 它最大限度地减少了这些输出分布之间的双向KL分歧.
- 理论分析支持R-Drop能够减少子结构不一致性,并弥合完全和子模型损失之间的差距.
主要成果:
- 7个深度学习任务和23个数据集的实验表明R-Drop的普遍有效性.
- 该方法在监督,参数效率和半监督学习下有利于前,反复和图形神经网络.
- 在WMT14英语 → 德语和英语 → 法语翻译任务中,R-Drop使用 vanila Transformer 模型获得了最先进的结果.
结论:
- 在深度学习中,R-Drop提供了一种简单而有效的方法来规范随机性.
- 它成功地减轻了培训-推理不一致性,从而提高了对具有挑战性的翻译任务的性能和最先进的结果.
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