Neural Network Weight Update via Augmented Image Difference

The patent applies parameter changes by modifying the data augmentation parameters (hue, brightness, saturation) during the training process. The neural network is trained with varying parameters to learn robust features that maintain stability across different conditions. This resolves the contradiction by enabling the system to handle variations beyond the predicted range while maintaining accuracy.

The patent implements dynamics by making the data augmentation parameters dynamic rather than static. The parameters change during training and can adapt to different input conditions. This dynamic approach allows the neural network to learn from a broader range of variations, improving both accuracy and stability across different environments.

The patent merges multiple data augmentation techniques (hue adjustment, brightness adjustment, saturation adjustment) into a unified training framework. By combining these techniques and applying them simultaneously during training, the system achieves robustness against various image variations without requiring separate training processes, thus managing complexity effectively.

The patent creates a universal training framework that handles multiple types of image variations (hue, brightness, saturation) through a single multi-functional data augmentation module. This universal approach improves robustness against different variations while avoiding the need for separate specialized training processes for each type of variation.