相关实验视频

使用前进前进算法训练卷积神经网络

Riccardo Scodellaro¹, Ajinkya Kulkarni², Frauke Alves^2,3,4

¹Translational Molecular Imaging, Max Planck Institute for Multidisciplinary Sciences, Hermann-Rein Straße 3, 37075, Göttingen, Germany. riccardo.scodellaro@mpinat.mpg.de.

Scientific reports

|November 4, 2025

概括

研究人员将前进前进 (FF) 算法适用于卷积神经网络 (CNN),表明它可以成功训练更深层的网络. 这种生物启发的方法为图像分析和神经形态计算提供了反向传播的有希望的替代方案.

科学领域:

人工智能的人工智能
计算机视觉计算机视觉
计算神经科学是一种神经科学.

背景情况:

深度神经网络,特别是卷积神经网络 (CNN),主导图像分析,主要通过反向传播 (BP) 进行训练.
杰弗里·欣顿 (Geoffrey Hinton) 提出了前进前进 (Forward-Forward,FF) 算法,作为一种生物学上可行的替代方案,利用局部良性函数和正负数据的联合呈现.
将FF扩展到CNN需要新的方法来跨空间位置传播标签信息.

研究的目的:

调整前向前向 (FF) 算法,用于卷积神经网络 (CNN).
为FF培训的CNN开发和评估空间扩展的标签策略.
调查FF训练CNN的学习动态,稳定性和特征学习能力.

主要方法:

引入了两个空间扩展的标签策略: 里埃图案和形态转换.
应用这些策略来训练使用FF算法对CIFAR10和CIFAR100数据集进行更深入的CNN.
使用类激活地图 (CAM) 来分析已学习的特征.

主要成果:

在图像数据集上成功优化了更深层的FF训练CNN.
展示了基于形态的标签可以有效地防止复杂数据集的捷径解决方案.
确认FF培训有效地扩展到100个班级 (CIFAR100),FF-CNNs在各层学习有意义的,互补的特征.

结论:

对于卷积神经网络来说,FF培训是可行的和有效的,它超越了完全连接的架构.
空间扩展的标签策略使FF CNN能够有效地学习并避免捷径解决方案.
经过FF训练的CNN表现出生物学上可信的学习动态,并具有神经形态计算的潜力.

关键词:

在美国,CNN是CNN.阶级激活地图.可解释的人工智能前进的前进的前进

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