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Updated: Jul 16, 2025
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Deep Neural Networks for Image-Based Dietary Assessment
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一个可扩展的二次优化器,具有适应性信任区域,用于神经网络
Donghee Yang1, Junhyun Cho2, Sungchul Lee1
1Department of Mathematics, Yonsei University, Seoul 03722, Republic of Korea.
概括
一个新的优化器Tadam通过高效地近似二级信息来增强深度学习. 这种信任区域自适应时刻估计方法提供了稳定和快速的融合,优于现有的优化器.
科学领域:
- 机器学习 机器学习
- 优化算法 优化算法
背景情况:
- 第二阶优化方法提供了改进的梯度估计,但由于大矩阵反转,它面临着计算成本和内存使用方面的挑战.
- 现有的优化器通常依赖于一阶近似,限制了复杂的深度学习模型中的融合速度和稳定性.
研究的目的:
- 介绍Tadam (信任区域 ADAptive Moment估计),一种新的二级优化算法,旨在克服传统二级方法的计算和内存限制.
- 开发一个优化器,有效地接近费舍尔信息矩阵,并采用适应性信任区域,以提高稳定性和性能.
主要方法:
- 塔达姆使用费舍尔信息矩阵的合理近似方法将损失近似到第二顺序,将计算复杂度降低到O(N).
- 实施了适应性信任区域方案,以根据损失最小化进展动态调整信任区域大小,减轻近似错误并确保稳定性.
- 优化器内部调整学习速率,即使提供固定学习速率,也提供自适应式学习速率控制.
主要成果:
- 实验表明,Tadam在速度和稳定性方面表现优于Adam,AMSGrad,Radam和Nadam等已建立的优化器.
- 塔达姆实现了与基准优化器相比的空间和时间复杂性,同时提供了卓越的性能.
- 提出的方法成功地在各种实验设置中有效和稳定地找到合理的解决方案.
结论:
- 塔达姆提出了一种计算效率高,稳定的二阶优化方法,适合深度学习应用.
- 适应性信任区域和费舍尔信息矩阵近似策略有效地解决了二级优化的实际挑战.
- 塔达姆为现有优化器提供了一个有前途的替代方案,提供更快,更可靠的融合.
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