What does "Coarse-to-fine Training" mean?
Table of Contents
Coarse-to-fine training is a method used to improve the way models learn from data. Instead of starting with detailed, high-quality information, this approach begins with simpler, less detailed data. This helps the model grasp the basic patterns before moving on to more complex details.
How It Works
- Initial Learning: The model first trains on lower-resolution or less detailed data. This stage is easier and requires less computing power.
- Refinement: Once the model understands the basics, it is then trained with high-resolution or detailed data. This helps the model improve its skills and understand finer details.
Benefits
- Faster Training: By starting with simpler data, models can learn more quickly.
- Less Resource-Intensive: This method requires less computing power and time compared to starting with complex data right away.
- Broad Use: Coarse-to-fine training can be applied to various models, making it a useful strategy in different fields.