Generative Adversarial Network and Score-Based Generative Model Comparison
Ruoxuan Zhu · 2023
Generative models are a category of machine learning models that can generate new data by studying the underlying distribution of an existing dataset. Deep generative models are a specific type of generative model that use deep neural networks to capture intricate patterns in the probability distribution of the dataset. These models have the potential to generate highly realistic and diverse data. However, when the quantity of available data is limited, as is frequently the case in real-world applications, generating new data with deep generative models can be a challenging task. To address this issue, various techniques have been suggested to train deep generative models, such as using Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). While these techniques have shown potential in improving deep generative models' performance, their effectiveness varies depending on various factors, including the size and nature of the dataset. Therefore, further research is necessary to develop more effective and reliable techniques to train deep generative models for various practical applications. Score-based generative models are a class of generative models that estimate the score function and use it to generate samples through the Langevin dynamics algorithm. This article focuses on comparing GANs and score-based generative models based on the CIFAR-10 dataset. The article discovered the complementary aspect of GANs and Score-Based Generative Models and suggested improvements in training both models. The results allows for a more stable and more flexible training process, the combination training has shown promising results in generating high-quality and diverse samples in various applications.