Using a pre-trained GAN like StyleGAN-XL to visualize learned representations in CNNs may introduce several limitations and biases. One significant limitation is the potential mismatch between the data distribution of the GAN and that of the CNN. If the GAN is trained on a dataset that does not adequately represent the classes or features present in the CNN's training data, the generated images may not accurately reflect the learned representations, leading to misleading interpretations.
Moreover, GANs, including StyleGAN-XL, can exhibit biases inherent in their training data. If the GAN is trained on a dataset with imbalanced class distributions or biased representations, these biases may be reflected in the visualizations, potentially skewing the analysis of the CNN's learned features. This could result in an incomplete or distorted understanding of the classifier's decision-making process.
Additionally, the reliance on a single generative model may limit the diversity of visualizations. Different GAN architectures or training strategies might yield different insights, and using only one model could overlook alternative representations that could be informative. Lastly, the interpretability of the generated images is contingent on the quality of the GAN's output; if the GAN fails to produce high-fidelity images, the visualizations may not effectively convey the learned features of the CNN.