To stabilize the training of GANs and avoid model divergence when dealing with high-frequency data, cyclical learning rates (CLR) can be utilized. You can also follow the following steps:
- Learning Rate Range: Set a minimum and maximum learning rate for the cycle. The learning rate oscillates between these bounds.
- Warm-up Phase: Start with a lower learning rate and gradually increase it to the maximum, allowing the model to stabilize at the beginning of training.
- Cyclic Schedules: Use cyclic policies like triangular, cosine annealing, or sine wave to change the learning rate throughout training, which helps in exploring different parts of the loss landscape.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Cyclical Learning Rate: Oscillates the learning rate between a base and maximum value, helping avoid model divergence.
- Triangular Policy: A triangular cyclical schedule allows the learning rate to increase and decrease during training for better exploration.
- Warm-up: Starts with a lower learning rate, gradually increasing it to prevent instability during the initial phase.
- Stabilization in High-Frequency Data: Helps the model cope with high-frequency data by avoiding sharp learning rate spikes, thus promoting stable convergence.
Hence, by referring to the above, you can use cyclical learning rates to stabilize the training of GANs while avoiding model divergence in high-frequency data.
