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Pages tagged generative-models
📄 **[Read on arXiv](https://arxiv.org/abs/2006.11239)** Ho, Jain, and Abbeel, NeurIPS, 2020. - [Paper](https://arxiv.org/abs/2006.11239) Denoising Diffusion Probabilistic Models (DDPM) demonstrates that high-quality ima…
📄 **[Read on arXiv](https://arxiv.org/abs/2105.05233)** This paper by Dhariwal and Nichol (OpenAI, 2021) demonstrates that diffusion models can surpass GANs on image synthesis for the first time, achieving state-of-the-…
📄 **[Read on arXiv](https://arxiv.org/abs/2204.06125)** DALL-E 2 (internally called unCLIP) introduces a hierarchical approach to text-conditional image generation that leverages CLIP's joint text-image embedding space…
📄 **[Read on arXiv](https://arxiv.org/abs/2112.10752)** Latent Diffusion Models (LDMs), the architecture behind Stable Diffusion, address the prohibitive computational cost of applying diffusion models directly in pixel…
📄 **[Read on arXiv](https://arxiv.org/abs/2404.15014)** OccGen reframes 3D semantic occupancy prediction as a conditional generative problem rather than a purely discriminative one. Prior occupancy methods (SurroundOcc,…
📄 **[Read on arXiv](https://arxiv.org/abs/1611.02731)** The Variational Lossy Autoencoder (VLAE) by Chen, Kingma, Salimans, Duan, Dhariwal, Schulman, Sutskever, and Abbeel (2016) addresses the fundamental tension in VAE…