GANs use two neural networks to generate data that looks like real data, while diffusion models use a series of de-noising steps to progressively cut the noise and reveal an image that belongs to the training data's distribution.
Diffusion models provide training stability and quality results on image and audio generation
, while GANs are better at mapping a random noisy image to a point in the training distribution.
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GAN is an algorithmic architecture that uses two neural networks that are set one against the other to generate newly synthesised instances of data that can pass for real data. Diffusion models have become increasingly popular as they provide training stability as well as quality results on image and audio generation.
Diffusion Models Vs GANs: Which one to choose for Image Synthesis
Unlike GANs which learn to map a random noisy image to a point in the training distribution, diffusion models take a noisy image and then perform a series of de-noising steps that progressively cut the noise and reveal an image that belongs to the training data's distribution.
OpenAI's diffusion models beat GANs at what they do best
Flexible models can fit arbitrary structures in data, but evaluating, training, or sampling from these models is usually expensive. Diffusion models are both analytically tractable and flexible. Cons: Diffusion models …
What are Diffusion Models? | Lil'Log - GitHub Pages
GANs capture less diversity than state-of-the-art likelihood-based models. Google AI has introduced two connected approaches to enhance the image synthesis quality for diffusion models: Super-Resolution via Repeated Refinements (SR3) and …
Are Google’s New Diffusion Models Better Than GANs?
VAE vs GAN VAE是直接计算生成图片和原始图片的均方误差而不是像 GAN 那样去对抗来学习，这就使得生成的图片会有点模糊。 但是VAE的收敛性要优于 GAN 。 因此又有 GAN hybrids：一方面可以提高VAE的采样质量和改善表示学习，另一方面也可以提高 GAN 的稳定性和丰富度。 上图给出了VAE和 GAN 的联系和区别。 无论是VAE还是 GAN ，我们在接触一个新模型的时候都需要注意以下几点： 1.网络结构和训练流程。 2.Loss。 3.随机性的引入方法。 其中，第3点是生成模型特有的，必须加倍重视。 参考： https://mp.weixin.qq.com/s/d_P-4uQx0kC2w6J69OZIAw Deepmind研究科学家最新演讲：VAEs and GANs
VAE（三）——VAE vs GAN, VAE参考资源