This technique involves using an existing trained model as a starting point for a new task. It saves time and computational resources by reusing already learned features from existing models to perform new tasks.
This technique involves using an existing trained model as a starting point for a new task. It saves time and computational resources by reusing already learned features from existing models to perform new tasks.