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

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Can you name the strategies used to optimize learning rates scheduled to prevent overfitting or underfitting in generative models?
Nov 8 in Generative AI by Ashutosh
• 4,690 points
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1 answer to this question.

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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.

answered Nov 8 by anila k

edited Nov 11 by Ashutosh

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