  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.
