探索协同集成学习：联合CNN、MLP-Mixer与视觉Transformer以增强图像分类
Exploring Synergistic Ensemble Learning: Uniting CNNs, MLP-Mixers, and Vision Transformers to Enhance Image Classification
AI总结：
本研究针对图像分类中不同架构的互补性问题，提出保留CNN、MLP-Mixer和视觉Transformer各自完整性并通过集成技术组合的方法，所构建的集成框架性能优于相似架构集成，在ImageNet上超越现有单模型基准且延迟更低。
摘要：
近年来，卷积神经网络（CNNs）、MLP-Mixer和视觉Transformer已成为图像分类领域的主流神经架构。先前研究强调了每种架构的独特优势，且越来越多的证据表明，结合不同架构的模块可提升性能。本研究在探索不同架构互补性的先前工作基础上进行改进：不再通过试错法启发式地合并不同架构的模块，而是保留每个架构的完整性，并使用集成技术将它们组合起来。通过保持每个架构的独特性，我们旨在更深入且隐式隔离地探索其内在互补性，此方法能更系统地理解它们的个体优势。此外，我们展示了即使是结合不同架构模型的基础集成方法也具有有效性，这些方法优于由相似架构组成的集成。我们简洁的集成框架可作为融合互补架构的基础策略，为进一步研究不同架构及其集成在图像分类中的独特优势和协同作用提供坚实起点。这项工作的直接成果是创建了一个分类网络集成，其在ImageNet上的准确率超过了之前最先进的单一分类网络，树立了新基准，同时所需总延迟更低。
英文摘要：
In recent years, Convolutional Neural Networks (CNNs), MLP-mixers, and Vision Transformers have risen to prominence as leading neural architectures in image classification. Prior research has underscored the distinct advantages of each architecture, and there is growing evidence that combining modules from different architectures can boost performance. In this study, we build upon and improve previous work exploring the complementarity between different architectures. Instead of heuristically merging modules from various architectures through trial and error, we preserve the integrity of each architecture and combine them using ensemble techniques. By maintaining the distinctiveness of each architecture, we aim to explore their inherent complementarity more deeply and with implicit isolation. This approach provides a more systematic understanding of their individual strengths. In addition to uncovering insights into architectural complementarity, we showcase the effectiveness of even basic ensemble methods that combine models from diverse architectures. These methods outperform ensembles comprised of similar architectures. Our straightforward ensemble framework serves as a foundational strategy for blending complementary architectures, offering a solid starting point for further investigations into the unique strengths and synergies among different architectures and their ensembles in image classification. A direct outcome of this work is the creation of an ensemble of classification networks that surpasses the accuracy of the previous state-of-the-art single classification network on ImageNet, setting a new benchmark, all while requiring less overall latency.
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。
