While building a model to generate images of people in different occupations, you find the training data is biased and some attributes skew the outputs. Which mitigation technique addresses this issue?

Data augmentation for imbalanced classes is the most direct mitigation technique. If certain attributes (e.g., specific occupations, genders, or ethnicities) are underrepresented in the training data, augmenting these underrepresented classes by creating new, synthetic variations of existing samples can help balance the dataset. This reduces the model's tendency to skew outputs towards overrepresented attributes. Model monitoring for class distribution is a detection method, not a mitigation technique. It helps identify the bias but doesn't fix it. Retrieval Augmented Generation (RAG) is a technique primarily used in natural language processing to enhance generation with retrieved information, not for addressing image generation bias. Watermark detection for images is used to identify embedded information or ownership, unrelated to mitigating data bias in image generation.