[论文解读] Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models

Neelesh Mungoli|arXiv (Cornell University)|Apr 4, 2023

Domain Adaptation and Few-Shot Learning被引用 40

一句话总结

自适应特征融合（AFF）动态融合多源特征，以提升在 CNN、RNN 和 GCN 上的泛化能力，超过传统融合方法。

ABSTRACT

In recent years, deep learning models have demonstrated remarkable success in various domains, such as computer vision, natural language processing, and speech recognition. However, the generalization capabilities of these models can be negatively impacted by the limitations of their feature fusion techniques. This paper introduces an innovative approach, Adaptive Feature Fusion (AFF), to enhance the generalization of deep learning models by dynamically adapting the fusion process of feature representations. The proposed AFF framework is designed to incorporate fusion layers into existing deep learning architectures, enabling seamless integration and improved performance. By leveraging a combination of data-driven and model-based fusion strategies, AFF is able to adaptively fuse features based on the underlying data characteristics and model requirements. This paper presents a detailed description of the AFF framework, including the design and implementation of fusion layers for various architectures. Extensive experiments are conducted on multiple benchmark datasets, with the results demonstrating the superiority of the AFF approach in comparison to traditional feature fusion techniques. The analysis showcases the effectiveness of AFF in enhancing generalization capabilities, leading to improved performance across different tasks and applications. Finally, the paper discusses various real-world use cases where AFF can be employed, providing insights into its practical applicability. The conclusion highlights the potential for future research directions, including the exploration of advanced fusion strategies and the extension of AFF to other machine learning paradigms.

研究动机与目标

• 阐明在深度学习中需要更具泛化性的特征融合。
• 提出自适应特征融合（AFF）框架，以动态融合来自多个源的特征。
• 描述自适应融合层的设计及其在各种架构中的集成。
• 展示 AFF 在多任务和多数据集上相对于传统融合技术的优势。

提出的方法

• 引入接收来自多个源特征的自适应融合层（如不同网络层）。
• 使用数据驱动策略（如注意力、基于图的技术）来学习融合权重。
• 结合模型驱动的引导，使融合与任务和架构的特性保持一致。
• 通过辅助目标使用元学习组件来优化跨任务的融合输出。
• 整合正则化（ dropout、权重衰减、辅助任务）以提升泛化能力。
• 用标准的反向传播训练增强后的架构，以学习融合函数和元学习器。

实验结果

研究问题

• RQ1 AFF 是否能够通过自适应地融合来自多个源的特征来提升泛化能力？
• RQ2 当数据驱动与模型驱动的融合策略结合时，是否在跨领域上优于传统的融合方法？
• RQ3 AFF 如何影响图像分类、目标检测、情感分析和图分类的性能？

主要发现

• AFF 在所测试的任务中始终优于传统融合方法（拼接、相加、相乘）。
• 在图像分类、目标检测、情感分析和图分类中，AFF 在准确率、精确率、召回率、F1、IoU 或 macro-F1（视任务而定）方面达到更高的指标。
• 改进归因于自适应融合、数据驱动与模型驱动策略的结合，以及元学习组件。
• 通过模块化融合块，自适应融合层与 CNNs、RNNs、GCNs 兼容。