What strategies do you use to optimize learning rate schedules to prevent overfitting or underfitting in generative models

Question

Can you name the strategies used to optimize learning rates scheduled to prevent overfitting or underfitting in generative models?

Answer 1

You can optimize learning rates scheduled to prevent overfitting or underfitting by following these strategies:

• Learning Rate Warmup: Gradually increases the learning rate from a small initial value to the target learning rate over a few epochs to stabilize training.
• Step Decay: Reduces the learning rate by a fixed factor at predefined steps or epochs, typically after a set number of iterations.
• Exponential Day: Decreases the learning rate exponentially over time, typically by a fixed multiplicative factor per epoch.
• Cosine Annealing: Reduces the learning rate following a concise curve, starting high and slowly decreasing to a minimum, often with restarts.
• Reduce on Plateau: Lowers the learning rate when a metric stops improving for a specified number of epochs, helping avoid stagnant training.

These strategies above will balance effective learning, leading to the prevention of overfitting or underfitting.