Abstract：
Recent image classification efforts have achieved certain success by incorporating prior information such as labels and logical rules to learn discriminative features. However, these methods overlook the variability of features, resulting in feature inconsistency and fluctuations in model parameter updates, which further contribute to decreased image classification accuracy and model instability. To address this issue, this paper proposes a novel method combining structural prior-driven feature extraction with gradient-momentum (SPGM), from the perspectives of consistent feature learning and precise parameter updates, to enhance the accuracy and stability of image classification. Specifically, SPGM leverages a structural prior-driven feature extraction (SPFE) approach to calculate gradients of multi-level features and original images to construct structural information, which is then transformed into prior knowledge to drive the network to learn features consistent with the original images. Additionally, an optimization strategy integrating gradients and momentum (GMO) is introduced, dynamically adjusting the direction and step size of parameter updates based on the angle and norm of the sum of gradients and momentum, enabling precise model parameter updates. Extensive experiments on CIFAR10 and CIFAR100 datasets demonstrate that the SPGM method significantly reduces the top-1 error rate in image classification, enhances the classification performance, and outperforms state-of-the-art methods.
摘要：
通过结合诸如标签和逻辑规则之类的先验信息来学习有区别的特征，最近的图像分类工作取得了一定的成功。然而,这些方法忽略了特征的可变性，导致特征不一致和模型参数更新的波动，这进一步降低了图像分类的准确性和模型的不稳定性。为了解决这个问题，本文提出了一种将结构先验驱动特征提取与梯度动量(SPGM)相结合的新方法,从一致的特征学习和精确的参数更新的角度来看，提高图像分类的准确性和稳定性。具体来说,SPGM利用结构先验驱动的特征提取（SPFE）方法来计算多级特征和原始图像的梯度，以构建结构信息，然后将其转化为先验知识，以驱动网络学习与原始图像一致的特征。此外,引入了梯度和动量(GMO)集成优化策略，根据梯度和动量之和的角度和范数，动态调整参数更新的方向和步长，启用精确的模型参数更新。在CIFAR10和CIFAR100数据集上进行的大量实验表明，SPGM方法显着降低了图像分类中的前1位错误率，提高分类性能，并优于最先进的方法。
