Mode collapse in Generative Adversarial Networks (GANs) 🖼️ is a common problem where the generator fails to produce diverse samples, instead focusing on a limited subset of the data distribution. To address this issue, various techniques have been proposed. Wasserstein GAN introduces the Wasserstein distance to measure the difference between the generated and real distributions, providing more stable training. Conditional GAN incorporates additional information such as class labels to condition the generation process, encouraging diversity. Progressive GAN gradually increases the complexity of both the generator and discriminator, enabling the generation of high-resolution images while maintaining stability. These techniques collectively contribute to mitigating mode collapse and enhancing the performance of GANs. 🛠️

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