SYNTHESIZE & LEARN: JOINTLY OPTIMIZING GENERATIVE AND CLASSIFIER NETWORKS FOR IMPROVED DROWSINESS DETECTION

Driving in a state of drowsiness is a major cause of road accidents, resulting in tremendous damage to life and property. Developing robust, automatic, real-time systems that can infer drowsiness states of drivers has the potential of making life-saving impact. However, real-world drowsy driving datasets are unbalanced, due to the sparsity of drowsy driving events. We focus on the problem of alleviating the class imbalance problem by using generative adversarial networks (GAN) to synthesize examples of sparse classes directly in the feature-space. Our GAN-based framework simultaneously generates realistic examples of sparse classes while using the generated samples to improve the performance of a separate drowsiness classifier. We validate this approach in a real-world drowsiness dataset, where we demonstrate a classifier trained using this approach outperforms a stand-alone classifier trained without any GAN-based augmentations.