在飞行中调制,以实现平衡的多式模式学习
IEEE transactions on pattern analysis and machine intelligence
|September 25, 2024
概括
多模式学习与不平衡的培训作斗争. 新的即时预测和梯度调制 (OPM/OGM) 策略平衡模式的影响,显著提高跨任务的模型性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 多模式学习整合了各种数据类型,以提高模型性能.
- 当前的联合培训方法往往导致由于占主导地位的模式而导致未经优化的单模式表示.
研究的目的:
- 解决多式模式学习中不平衡的模式优化问题.
- 在联合培训框架内制定新的战略,以改善单一模式代表性学习.
主要方法:
- 分析前和后传播阶段的低优化情况.
- 引入即时预测调制 (OPM) 来动态下降主导模式的特征.
- 引入即时梯度调制 (OGM) 以减轻主导模式的梯度.
主要成果:
- 在培训过程中,OPM和OGM有效地平衡了不同模式的影响.
- 在各种多式联运任务中表现出显著的性能改进.
- 在基本和复杂的多式联运模型中展示了增强的性能.
结论:
- 拟议的OPM和OGM策略是对不平衡的多式联络学习的有效和灵活的解决方案.
- 这些方法提供了一种简单而有力的方法来提高单模表示质量.
- 这些发现表明了优化多式模式学习架构的新方向.
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