Description: Neural fusion refers to the integration of multiple neural networks or their outputs to enhance the accuracy and robustness of machine learning models. This approach allows for the combination of the strengths of different network architectures, leveraging their unique capabilities to tackle complex problems. Fusion can occur at the feature level, where intermediate representations from the networks are combined, or at the decision level, where the final outputs of each network are integrated to achieve a more reliable prediction. This method is particularly useful in tasks where data variability can impact the performance of a single model, such as image recognition, natural language processing, and recommendation systems. Neural fusion not only improves accuracy but can also increase model generalization, reducing the risk of overfitting. Additionally, it enables the creation of more robust systems that can adapt to different contexts and data types, which is essential in various real-world applications where variability is the norm.
