Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.
Data Augmentation for Imbalanced Classes:
Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.
Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.
Why Option A is Correct:
Balances Data Distribution: Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.
Improves Model Fairness: Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.
Why Other Options are Incorrect:
B. Model monitoring for class distribution: Helps identify bias but does not actively correct it.
C. Retrieval Augmented Generation (RAG): Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.
D. Watermark detection for images: Detects watermarks in images, not a technique for addressing bias.
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