  The demand for high-speed data is exponentially growing. To conquer this,
optical networks underwent significant changes getting more complex and
versatile. The increasing complexity necessitates the fault management to be
more adaptive to enhance network assurance. In this paper, we experimentally
compare the performance of soft-failure management of different machine
learning algorithms. We further introduce a machine-learning based soft-failure
management framework. It utilizes a variational autoencoder based generative
adversarial network (VAE-GAN) running on optical spectral data obtained by
optical spectrum analyzers. The framework is able to reliably run on a fraction
of available training data as well as identifying unknown failure types. The
investigations show, that the VAE-GAN outperforms the other machine learning
algorithms when up to 10\% of the total training data is available in
identification tasks. Furthermore, the advanced training mechanism for the GAN
shows a high F1-score for unknown spectrum identification. The failure
localization comparison shows the advantage of a low complexity neural network
in combination with a VAE over established machine learning algorithms.
