📄 中文摘要
生成对抗网络（GANs）与逆向学习和能量模型之间存在着有趣的联系。GANs由两个部分组成，一个生成图像，另一个对图像进行评估。在共同训练的过程中，研究者发现了GANs与人类学习行为背后隐含原因的逆向学习之间的关联。同时，能量模型通过简单的评分机制来评估事物的优劣，进一步融入了这一框架。这些方法可以被视为同一思想的不同表现形式，分别尝试复制示例和推测产生示例的规则，并且都可以看作是设定或读取一种评分机制。这种视角有助于解决训练过程中的问题。
生成对抗网络、逆向强化学习与能量模型之间的联系
📄 English Summary
A Connection between Generative Adversarial Networks, Inverse ReinforcementLearning, and Energy-Based Models
A connection has been found between Generative Adversarial Networks (GANs), inverse learning, and energy models. GANs consist of a generator that creates images and a discriminator that evaluates them. When trained together, researchers uncovered a link between GANs and inverse reinforcement learning, which seeks to understand the hidden motivations behind actions. Additionally, energy models, which use a simple scoring system to assess quality, fit into this framework. These methods can be viewed as different manifestations of the same underlying concept, where one part aims to replicate examples while the other infers the rules that generated those examples, both functioning as a means of setting or interpreting a score. This perspective aids in addressing challenges during the training process.