IGAN：推理式生成对抗网络
IGAN: Inferent and Generative Adversarial Networks
AI总结：
本文提出IGAN，通过在图像与潜在空间建立双向映射并引入纠缠对抗博弈，在保持生成质量的同时提升GAN的稳定性与收敛性，并展示其在自监督和多模态转换中的应用潜力。
摘要：
我提出IGAN（Inferent Generative Adversarial Networks，推理式生成对抗网络），这是一种神经架构，能够在复杂的高维数据分布上同时学习生成模型和推理模型，即在数据样本与更简单的低维潜在空间之间建立双向映射。它通过在图像空间和潜在空间中重写对抗策略，以数据-潜在编码后验与先验之间的纠缠博弈，将推理能力扩展到传统GAN框架中。它在保持生成质量的同时，为经典GAN方案带来了可衡量的稳定性和收敛性，并且保持简单和节俭，以便在实验室PC上运行。IGAN促使编码后的潜在变量覆盖整个先验空间：这使得能够以无监督方式利用一个扩大且自组织的潜在空间。对先前发表文章的分析为所提算法奠定了理论基础。在包括SAR和光学影像在内的常见图像数据集上，给出了自监督或多模态数据转换等潜在应用的定性演示。
英文摘要：
I present IGAN (Inferent Generative Adversarial Networks), a neural architecture that learns both a generative and an inference model on a complex high dimensional data distribution, i.e. a bidirectional mapping between data samples and a simpler low-dimensional latent space. It extends the traditional GAN framework with inference by rewriting the adversarial strategy in both the image and the latent space with an entangled game between data-latent encoded posteriors and priors. It brings a measurable stability and convergence to the classical GAN scheme, while keeping its generative quality and remaining simple and frugal in order to run on a lab PC. IGAN fosters the encoded latents to span the full prior space: this enables the exploitation of an enlarged and self-organised latent space in an unsupervised manner. An analysis of previously published articles sets the theoretical ground for the proposed algorithm. A qualitative demonstration of potential applications like self-supervision or multi-modal data translation is given on common image datasets including SAR and optical imagery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
