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Nov, 2022
基于经验的优化器选择策略研究: 面向非分布式环境下的广义泛化问题
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
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Hiroki Naganuma, Kartik Ahuja, Ioannis Mitliagkas, Shiro Takagi, Tetsuya Motokawa...
TL;DR
研究不同类型的分布偏移下用于图像和文本分类的常用优化器的性能,发现自适应优化器表现较差,并且在分布偏移对分类准确性的影响方面呈现三种类别的行为,可以帮助实践者选择正确的优化器。
Abstract
Modern
deep learning
systems are fragile and do not generalize well under distribution shifts. While much promising work has been accomplished to address these concerns, a systematic study of the role of
optimizers
→