The paper introduces Least Squares Generative Adversarial Networks (LSGANs), a novel approach to improve the quality and stability of image generation in generative adversarial networks (GANs). Traditional GANs use the sigmoid cross entropy loss function for the discriminator, which can lead to vanishing gradients and mode collapse. LSGANs adopt the least squares loss function, which penalizes samples that are far from the decision boundary, making the generator more effective in generating samples closer to the real data distribution. The authors demonstrate that minimizing the objective function of LSGANs yields the Pearson $\chi^2$ divergence, leading to higher-quality images. Experimental results on various scene datasets and a handwritten Chinese character dataset show that LSGANs generate more realistic and readable images compared to regular GANs. Additionally, LSGANs exhibit better stability during the learning process, as evidenced by comparison experiments that exclude batch normalization and evaluate on a Gaussian mixture distribution dataset. The paper concludes with future directions, including extending LSGANs to more complex datasets and exploring methods to directly pull generated samples toward the real data distribution.The paper introduces Least Squares Generative Adversarial Networks (LSGANs), a novel approach to improve the quality and stability of image generation in generative adversarial networks (GANs). Traditional GANs use the sigmoid cross entropy loss function for the discriminator, which can lead to vanishing gradients and mode collapse. LSGANs adopt the least squares loss function, which penalizes samples that are far from the decision boundary, making the generator more effective in generating samples closer to the real data distribution. The authors demonstrate that minimizing the objective function of LSGANs yields the Pearson $\chi^2$ divergence, leading to higher-quality images. Experimental results on various scene datasets and a handwritten Chinese character dataset show that LSGANs generate more realistic and readable images compared to regular GANs. Additionally, LSGANs exhibit better stability during the learning process, as evidenced by comparison experiments that exclude batch normalization and evaluate on a Gaussian mixture distribution dataset. The paper concludes with future directions, including extending LSGANs to more complex datasets and exploring methods to directly pull generated samples toward the real data distribution.