论文标题
对抗训练改善了基于共同能量的生成建模
Adversarial Training Improves Joint Energy-Based Generative Modelling
论文作者
论文摘要
我们提出了使用基于混合能量的模型的生成建模的新型框架。在我们的方法中，我们将可解释的输入梯度和用于采样的Langevin动力学的可解释输入梯度结合在一起。使用对抗性训练，我们不仅改善了训练稳定性，而且改善了联合能量模型的鲁棒性和生成性建模。
We propose the novel framework for generative modelling using hybrid energy-based models. In our method we combine the interpretable input gradients of the robust classifier and Langevin Dynamics for sampling. Using the adversarial training we improve not only the training stability, but robustness and generative modelling of the joint energy-based models.
