Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 37 discussion

Data augmentation for imbalanced classes: If the input data is biased and leads to undesirable attributes in the generated images (such as certain professions being overrepresented by specific attributes like gender or race), data augmentation can help balance the dataset. Data augmentation involves creating new training samples by applying transformations like cropping, rotating, or altering color schemes to existing data. This can help create a more diverse, balanced dataset and reduce bias by ensuring the model sees a more representative set of examples.

Data augmentation for imbalanced classes Data augmentation techniques can help mitigate bias in image generation models by artificially increasing the diversity of the training data. By applying transformations like rotations, flips, and color jittering to existing images, you can create new, synthetic images that are similar to the original ones. This can help balance the dataset and reduce the impact of biases present in the original data.

A. Data augmentation for imbalanced classes is the most effective technique to mitigate bias in the input data by ensuring a more balanced representation of classes and attributes in the training set, leading to fairer and more accurate image generation.