Title: ON INJECTING NOISE DURING INFERENCE. Abstract: We study activation noise in a generative energy-based modeling setting during training for the purpose of regularization. We prove that activation noise is a general form of dropout. Then, we analyze the role of activation noise at inference time and demonstrate it to be utilizing sampling. Thanks to the activation noise we observe about 200% improvement in performance (classification accuracy). Later, we not only discover, but also prove that the best performance is achieved when the activation noise follows the same distribution both during training and inference. To explicate this phenomenon, we provide theoretical results that illuminate the roles of activation noise during training, inference, and their mutual influence on the performance. To further confirm our theoretical results, we conduct experiments for five datasets and seven distributions of activation noise.