Learning robust features by extended generative stochastic networks

Deep neural networks have achieved state-of-the-art performance on many object recognition tasks, but they are vulnerable to small adversarial perturbations. In this paper, several extensions of generative stochastic networks (GSNs) are proposed to improve the robustness of neural networks to random noise and adversarial perturbations. Experimental results show that compared to normal GSN method, the extensions using adversarial examples, lateral connections and feedforward networks can improve the performance of GSNs by making the models more resistant to overfitting and noise.