How do I handle unrealistic generated outputs despite optimizing hyperparameters

Question

With the help of code in Python, can you tell me how I handle unrealistic generated outputs despite optimizing hyperparameters?

Answer 1

To handle unrealistic generated outputs despite optimizing hyperparameters, try techniques like improving data quality, adding regularization, using better loss functions, and employing a more advanced model architecture.

Here is the code reference you can refer to:

In the above code, we are using the following:

• Improved Loss Functions: Use advanced loss functions like Wasserstein loss to guide the model towards realistic outputs.
• Regularization: Apply L2 regularization to prevent overfitting and unrealistic outputs.
• Data Augmentation: Augment the training data (e.g., flipping images) to improve diversity and avoid generating unrealistic samples.

Hence, by referring to the above, you can handle unrealistic generated outputs despite optimizing hyperparameters

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