论文标题
用于选择gan的新型框架
A Novel Framework for Selection of GANs for an Application
论文作者
论文摘要
生成对抗网络（GAN）是当前研究的焦点。知识体是分散的，导致了试验方法，同时为给定情况选择了适当的gan。我们提供了一个全面的摘要，内容涉及gan的发展，从其诞生开始，解决了模式崩溃，消失的梯度，不稳定的培训和非缔约性的问题。我们还从应用程序的角度，其行为和实施细节来比较各种gan。我们提出了一个新颖的框架，以根据建筑，损失，正则化和差异来识别特定用例的候选剂量。我们还使用示例讨论了框架的应用，并证明了搜索空间的显着降低。这种确定潜在gans的有效方法降低了组织的AI发展的单位经济学。
Generative Adversarial Network (GAN) is a current focal point of research. The body of knowledge is fragmented, leading to a trial-error method while selecting an appropriate GAN for a given scenario. We provide a comprehensive summary of the evolution of GANs starting from its inception addressing issues like mode collapse, vanishing gradient, unstable training and non-convergence. We also provide a comparison of various GANs from the application point of view, its behaviour and implementation details. We propose a novel framework to identify candidate GANs for a specific use case based on architecture, loss, regularization and divergence. We also discuss application of the framework using an example, and we demonstrate a significant reduction in search space. This efficient way to determine potential GANs lowers unit economics of AI development for organizations.
