神经可塑性模块化框架：垃圾与工业表面的跨域图像分类
Adaptive Modular Retrieval-Augmented Framework for Cross-Domain Image Classification
AI总结：
本文提出神经可塑性模块化分类器，结合ResNet-50与ViT及FAISS检索，通过动态增长模块适应数据复杂性，在垃圾和工业表面缺陷分类中超越静态模型，实现高准确性和适应性。
摘要：
高效准确地分类废弃物和工业表面缺陷，对于确保可持续的废弃物管理和维持质量控制的高标准至关重要。本文介绍了神经可塑性模块化分类器，这是一种新颖的混合架构，专为动态环境中的鲁棒性和适应性图像分类而设计。该模型结合了用于局部特征提取的ResNet-50骨干网络和用于捕获全局语义上下文的视觉Transformer（ViT）。此外，还引入了基于FAISS的相似性检索，以提供对先前遇到数据的类似记忆的参考，从而丰富模型的特征空间。我们架构的一个关键创新是神经可塑性模块化设计，由可扩展、可学习的模块组成，这些模块在训练过程中性能达到平台期时会动态增长。受生物学习系统的启发，这种机制使模型能够随时间适应数据复杂性，提高泛化能力。除了垃圾分类，我们还在Kolektor表面缺陷数据集2（KolektorSDD2）上验证了该模型，该数据集涉及金属表面的工业缺陷检测。跨领域的实验结果表明，所提出的架构在准确性和适应性方面均优于传统的静态模型。神经可塑性模块化分类器为现实世界的图像分类提供了可扩展、高性能的解决方案，在环境和工业领域都具有很强的适用性。
英文摘要：
Effective image classification in real-world scenarios requires models capable of capturing both local features and global context, while adapting to the evolving complexity of the input. This paper presents a hybrid deep learning system named AMR-Net (Adaptive Modular Retrieval Network) for adaptable image classification in environmental and industrial contexts. The proposed model integrates ResNet-based local feature extraction and Vision Transformer-based global representation learning with an FAISS-based memory retrieval module for contextual enhancement. Furthermore, AMR-Net has an adaptive modular extension mechanism to dynamically enhance network capacity when validation performance plateaus during training. The proposed framework was evaluated using a ten-class waste classification dataset and the Kolektor Surface Defect Dataset 2 (KolektorSDD2), achieving classification accuracies of 97.15% and 99.80%, respectively. Experimental results demonstrate that AMR-Net consistently outperforms mainstream convolutional and transformer-based approaches while maintaining robust cross-domain generalization. Component-wise ablation studies show that global attention (ViT), retrieval-based contextual learning (FAISS), and adaptive modular growth each provide complementary and partially overlapping contributions to overall performance, with global attention contributing the largest individual gain. The proposed architecture is a scalable and flexible solution for image classification challenges characterized by data unpredictability, class imbalance, and dynamic feature distributions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
