While training neural networks is well-understood to be a key component in building intelligent AI systems, certain innovative techniques can enhance the efficiency and effectiveness of this crucial process. Beyond the typical use of vast and well-curated datasets or regularized training methodologies, there are some lesser-known, yet highly effective strategies employed by AI researchers and engineers around the world.

One intriguing approach is the use of synthetic data when real-world data is scarce or too sensitive to use. This technique involves generating artificial data that simulates real-world scenarios, which can help networks learn robust features without compromising privacy or security.

Another innovative strategy is transfer learning, where a model developed for one task is reused as the starting point for a model on another task. This not only speeds up the training process but also improves performance, especially when the new task has limited data available.

Lastly, adversarial training, where models are trained with data points specifically designed to confuse or trick the AI, has proven to be a unique method to enhance model robustness. This tough love approach makes neural networks less susceptible to errors when processing new, unexpected types of data.

These techniques showcase the evolving nature of AI training methodologies, which are continuously refined to meet the rising demands of technology and industry standards.