We evaluated models' performance in terms of fidelity, diversity, speed of training, and predictive ability of classifiers trained on the generated synthetic data. In addition we provided explainability through exploration of latent space and embeddings projection focused both on global and local explanations.
This work explored unconditional and conditional GANs to compare their bias inheritance and how the synthetic data influenced the models, and examined classification models trained on both real and synthetic data with counterfactual bias explanations.
We evaluated models' performance in terms of fidelity, diversity, speed of training, and predictive ability of classifiers trained on the generated synthetic data. In addition we provided explainability through exploration of latent space and embeddings projection focused both on global and local explanations.
We explained the classifier's diagnosis for skin cancer using both local and global techniques of explainable AI.