论文标题
变异自动编码器和潜在能量模型的联合培训
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
论文作者
论文摘要
本文提出了一种联合培训方法，同时学习变异自动编码器（VAE）和潜在能量模型（EBM）。 VAE和潜在EBM的联合培训是基于一个目标函数，该目标函数由潜在矢量和图像上的三个联合分布之间的三个kullback-leibler差异组成，并且目标函数具有优雅的对称和反对称形式的差异三角形三角形三角形三角形三角形三角形三角形，可以无缝地整合变异和对抗性学习。在这种联合培训方案中，潜在EBM充当了发电机模型的批评，而发电机模型和VAE中的推理模型则是潜在EBM的近似合成采样器和推理采样器。我们的实验表明，联合培训大大提高了VAE的合成质量。它还可以学习能够从样本示例中检测到异常检测的能量函数。
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). The joint training of VAE and latent EBM are based on an objective function that consists of three Kullback-Leibler divergences between three joint distributions on the latent vector and the image, and the objective function is of an elegant symmetric and anti-symmetric form of divergence triangle that seamlessly integrates variational and adversarial learning. In this joint training scheme, the latent EBM serves as a critic of the generator model, while the generator model and the inference model in VAE serve as the approximate synthesis sampler and inference sampler of the latent EBM. Our experiments show that the joint training greatly improves the synthesis quality of the VAE. It also enables learning of an energy function that is capable of detecting out of sample examples for anomaly detection.
