Description: Yin-Yang GAN is a Generative Adversarial Network (GAN) architecture that focuses on balancing the generator and discriminator to improve training stability and performance. This architecture is inspired by the concept of Yin-Yang, which symbolizes duality and balance in Chinese philosophy. In the context of GANs, the generator and discriminator are two neural networks that compete against each other: the generator tries to create data that is indistinguishable from real data, while the discriminator attempts to differentiate between real and generated data. Yin-Yang GAN aims to optimize this interaction by ensuring that both components develop in a balanced manner. This is achieved through techniques that dynamically adjust the learning capacity of each network, preventing one from dominating the training process. As a result, the quality of generated samples improves, and the risk of common issues in GAN training, such as mode collapse, is reduced. This architecture is particularly relevant in applications where the quality of generated data is critical, such as in generative modeling tasks. The implementation of Yin-Yang GAN represents a significant advancement in the quest for more robust and efficient generative models.
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