PcGAN: A Noise Robust Conditional Generative Adversarial Network for One Shot Learning

Abstract

Traffic sign classification plays a vital role in autonomous vehicles for its powerful capability in information representation. However, the low-quality data of traffic signs captured by in-vehicle cameras often inevitably bring inherent challenges to the one-shot classification task. Apart from the problem of data degradation, learning-based classification techniques of real traffic signs also come across the challenges of intra-class and inter-class data imbalance from the training data. To overcome the aforementioned problems, we propose an end-to-end degradation robust deep model, termed PcGAN, to classify traffic signs in a manner of few-shot learning. The proposed PcGAN models the joint distribution between the degraded traffic signal data and the corresponding prototypes from both degradation removal and generation perspectives by two alternating optimized modules, which ensures the generalization of the learned embedding of latent space for novel tasks. A multi-task loss function is designed to improve the robustness of PcGAN. Numerous experiments comprehensively demonstrate that the accuracy of our proposed PcGAN is improved by 5% compared with other state-of-the-art (SOTA) approaches in few-shot classification.