Neural Network Deep Supervised Learning Algorithm Based on Multimodal Data

In response to the problems of difficulty in information fusion, complex feature learning and difficulty in deep network training in multimodal data, a deep supervised learning algorithm is introduced. Through a multi-level supervision mechanism, it aims to improve the performance and generalization ability of neural networks in multimodal data processing. First, a deep supervised learning framework based on multi-modal information is proposed to jointly learn and fuse features of different modalities. Then, a hierarchical supervision mechanism is designed to introduce supervision signals at different levels of the network to enhance the model's understanding and utilization of the characteristics of each modality. Next, the improved Convolutional Neural Network (CNN) and LSTM (Long Short-Term Memory) structures are used to process image and text modal data. Finally, in order to avoid information loss, adversarial training is adopted to ensure that the fused features have good robustness and optimize the gradient flow during the training process. The multi-modal + text combination solution that introduces text information has significantly improved the model accuracy. The accuracy of some samples is as high as 0.95, far exceeding the effect of using images or multi-modal images alone. The multi-modal data processing framework based on deep supervised learning proposed in this study effectively solves the difficulties in information fusion and feature learning. Especially when processing heterogeneous data such as images and text, the generalization ability and robustness of neural networks are significantly improved. This method provides new ideas for the promotion of multimodal learning in practical applications.