Generative models

英文原文文件：generative_models.md

计算解释

VAE、GAN、自回归以及图像生成方法，其实际可用性取决于加速器吞吐量与采样成本。

支撑阅读卡

• Auto-Encoding Variational Bayes (2013, generative_media_compute)
• Generative Adversarial Nets (2014, generative_media_compute)
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (2015, generative_media_compute)
• Image-to-Image Translation with Conditional Adversarial Networks (2016, generative_media_compute)
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (2017, generative_media_compute)
• A Style-Based Generator Architecture for Generative Adversarial Networks (2018, generative_media_compute)
• Denoising Diffusion Probabilistic Models (2020, generative_media_compute)
• Score-Based Generative Modeling through Stochastic Differential Equations (2020, generative_media_compute)
• High-Resolution Image Synthesis with Latent Diffusion Models (2021, generative_media_compute)
• Zero-Shot Text-to-Image Generation (2021, generative_media_compute)
• Improved Denoising Diffusion Probabilistic Models (2021, generative_media_compute)
• Scalable Diffusion Models with Transformers (2022, generative_media_compute)
• Qwen3.5-Omni Technical Report (2026, generative_media_compute)

后续计算范式下过时或退居次要的内容

仅通过已链接的阅读卡追踪，不将本方法页视为独立证据来源。