论文标题
分解的对抗性推断
Decomposed Adversarial Learned Inference
论文作者
论文摘要
生成对抗模型的有效推断仍然是一个重要且具有挑战性的问题。我们提出了一种新颖的方法，分解了对抗性的推理（DALI），该推理明确匹配了数据和代码空间中的条件分布，并对生成模型的依赖关系结构进行了直接约束。我们得出了先验和条件匹配目标的等效形式，可以有效地优化，而没有任何参数假设。我们通过进行定量和定性评估来验证DALI对MNIST，CIFAR-10和CEELBA数据集的有效性。结果表明，与其他对抗推理模型相比，达利显着改善了重建和产生。
Effective inference for a generative adversarial model remains an important and challenging problem. We propose a novel approach, Decomposed Adversarial Learned Inference (DALI), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model. We derive an equivalent form of the prior and conditional matching objective that can be optimized efficiently without any parametric assumption on the data. We validate the effectiveness of DALI on the MNIST, CIFAR-10, and CelebA datasets by conducting quantitative and qualitative evaluations. Results demonstrate that DALI significantly improves both reconstruction and generation as compared to other adversarial inference models.
