# Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models

arXiv: https://arxiv.org/abs/2304.03290 · HTML: https://mlsanity.com/paper/2304.03290/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Discovering Reinforcement Learning Algorithms](https://mlsanity.com/paper/2007.08794/) (2020)
- [Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1811.11329/) (2018)
- [Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches](https://mlsanity.com/paper/1807.09427/) (2018)

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning-based Mixture of Vision Transformers for Video Violence Recognition](https://mlsanity.com/paper/2310.03108/) (2023)
- [Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models](https://mlsanity.com/paper/2406.16638/) (2024)
- [Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures](https://mlsanity.com/paper/2408.16442/) (2024)
- [A Dual-Feature Extractor Framework for Accurate Back Depth and Spine Morphology Estimation from Monocular RGB Images](https://mlsanity.com/paper/2507.22691/) (2025)
- [Enhancing Vehicle Detection under Adverse Weather Conditions with Contrastive Learning](https://mlsanity.com/paper/2509.21916/) (2025)

## Abstract (arXiv)

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.