Unsupervised Learning Using Generative Adversarial Networks on Micro-Doppler Spectrograms

This paper presents the implementation of a Generative Adversarial Network (GAN) and Adversarial Autoencoder (AAE) trained in an unsupervised manner using micro-Doppler (mD) spectrograms of human gait. Once the GAN network was trained, the domain where micro-Doppler feature learning happens is inspected. This domain is then accessed by building the AAE and different network visualizations are shown. The benefits of unsupervised training are highlighted by investigating the self-learned spectrogram features, revealing the potential of unsupervised adversarial training techniques for mD spectrogram feature learning methods.