Variational Diffusion Models
Authors: Diederik Kingma, Tim Salimans, Ben Poole, Jonathan Ho
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce a flexible family of diffusion-based generative models that achieve new state-of-the-art log-likelihoods on standard image density estimation benchmarks (CIFAR-10 and Image Net)... We empirically compare to these works, as well as others, in Table 1. |
| Researcher Affiliation | Industry | Google Research |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We demonstrate our proposed class of diffusion models, which we call Variational Diffusion Models (VDMs), on the CIFAR-10 [Krizhevsky et al., 2009] dataset, and the downsampled Image Net [Van Oord et al., 2016, Deng et al., 2009] dataset, where we focus on maximizing likelihood. |
| Dataset Splits | No | The paper mentions training and testing on datasets like CIFAR-10 and ImageNet, but it does not explicitly provide details about specific train/validation/test splits (e.g., percentages or sample counts) needed for reproduction beyond mentioning the 'test set'. |
| Hardware Specification | Yes | Our models are trained on NVIDIA A100 GPUs... |
| Software Dependencies | No | The paper states, 'We implemented our models in JAX [Bradbury et al., 2018] and Haiku [Anonymous, 2021],' but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use an Adam optimizer [Kingma and Ba, 2014] with 10−4 learning rate. We train for 2000K steps with a batch size of 2048. More details of our model specifications are in Appendix B. |