Using generative neural networks like Glow for data transformation has limitations, particularly when the latent space dimension is the same as the data space dimension. This can lead to computational challenges, especially when inverting matrices in high-dimensional spaces. To address this limitation, one approach is to downsample the data before training the generative model. By reducing the dimensionality of the input data, the computational complexity can be reduced, making it more feasible to train and use the generative model effectively.
Another limitation of using generative neural networks is the potential for overfitting, especially when dealing with complex and high-dimensional data. Regularization techniques can be employed during training to prevent overfitting and improve the generalization of the model. Additionally, ensemble methods or model averaging can be used to combine multiple generative models to enhance the robustness of the transformation process.