Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models

by   Neelesh Mungoli, et al.

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.


page 1

page 2

page 3

page 4


Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks

In this paper, we present an Adaptive Ensemble Learning framework that a...

Attention-Based Acoustic Feature Fusion Network for Depression Detection

Depression, a common mental disorder, significantly influences individua...

Hybrid ASR for Resource-Constrained Robots: HMM - Deep Learning Fusion

This paper presents a novel hybrid Automatic Speech Recognition (ASR) sy...

Explicit and implicit models in infrared and visible image fusion

Infrared and visible images, as multi-modal image pairs, show significan...

Model-based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era

This paper introduces and explores a new programming paradigm, Model-bas...

Comparing feature fusion strategies for Deep Learning-based kidney stone identification

This contribution presents a deep-learning method for extracting and fus...

Data-driven Knowledge Fusion for Deep Multi-instance Learning

Multi-instance learning (MIL) is a widely-applied technique in practical...

Please sign up or login with your details

Forgot password? Click here to reset