During multi-domain training the generator overfits to dominant domains How can domain balance be achieved

Question

Can i know During multi-domain training, the generator overfits to dominant domains. How can domain balance be achieved?

Answer 1

Use domain balancing techniques like weighted sampling, gradient balancing, or adversarial domain adaptation to prevent overfitting to dominant domains.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Weighted Sampling: Balances underrepresented domains by adjusting sampling probabilities.
• Adaptive Weighting: Dynamically assigns higher sampling probabilities to rare domains.
• Domain-Agnostic Training: Ensures all domains contribute equally to training.
• Scalability: Works with large datasets across multiple domains.

Hence, by leveraging weighted sampling, domain-specific loss balancing, and adversarial adaptation, multi-domain training can maintain equitable learning across all domains, preventing overfitting to dominant ones.