Radio astronomy is a vital tool for astronomers to study the Universe and has seen a wave of renewed interest and advancement over recent years. Next-generation radio telescope arrays like the SKA, ALMA and VLA are developed to be significantly more sensitive compared to older telescopes, which as a result also make them more susceptible to radio frequency interference (RFI). This highlights the need for effective RFI mitigation techniques in radio astronomy. We present a machine learning-based RFI mitigation approach that aims to separate RFI-corrupted spectrogram observations into signal of interest and RFI components in an unsupervised manner using a modified generative adversarial network (GAN) framework. We show that this unsupervised source separation approach is able to achieve performance comparable to a fully supervised approach.