Aiming at the problem of sample missing for magnetic flux leakage (MFL), a data reconstruction method based on conditional autoencoder (CVAE) and generative adversarial networks (GAN) is proposed.

These networks, which can be seen as variational autoencoders with a flexible latent prior, are smaller and easier to train than state-of-the-art generative models. As a second contribution, we ...