改进的VisNet模型提升物体识别与对称性分类准确率
Improving VisNet for Object Recognition
- 引入径向基函数神经元和马氏距离学习增强特征提取
- 在MNIST、CIFAR10等数据集上准确率显著优于基线模型
- 适合关注生物启发式视觉模型与可解释性研究的读者
物体识别在生物体感知与环境交互中起基础作用。尽管人类视觉系统表现出极高效率，但人工系统仍难以复现类似能力。本研究分析了生物启发的神经网络模型VisNet及其多个改进变体，结合径向基函数神经元、基于马氏距离的学习方法以及类视网膜预处理，用于通用物体识别与对称性分类。通过海布学习原则和时间连续性关联相邻视图，构建不变特征表示。在多个数据集（包括MNIST、CIFAR10及自定义对称物体集）上的实验表明，这些改进后的VisNet变体在识别准确率上显著优于基线模型。结果凸显了受生物启发架构的适应性与生物学相关性，为神经科学与人工智能中的视觉识别提供了强大且可解释的框架。
原文摘要 · Abstract (English)
Object recognition plays a fundamental role in how biological organisms perceive and interact with their environment. While the human visual system performs this task with remarkable efficiency, reproducing similar capabilities in artificial systems remains challenging. This study investigates VisNet, a biologically inspired neural network model, and several enhanced variants incorporating radial basis function neurons, Mahalanobis distance based learning, and retinal like preprocessing for both general object recognition and symmetry classification. By leveraging principles of Hebbian learning and temporal continuity associating temporally adjacent views to build invariant representations. VisNet and its extensions capture robust and transformation invariant features. Experimental results across multiple datasets, including MNIST, CIFAR10, and custom symmetric object sets, show that these enhanced VisNet variants substantially improve recognition accuracy compared with the baseline model. These findings underscore the adaptability and biological relevance of VisNet inspired architectures, offering a powerful and interpretable framework for visual recognition in both neuroscience and artificial intelligence. Keywords: VisNet, Object Recognition, Symmetry Detection, Hebbian Learning, RBF Neurons, Mahalanobis Distance, Biologically Inspired Models, Invariant Representations
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