Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation
Authors: Diederik Kingma, Ruiqi Gao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we explore new monotonic weightings and demonstrate their effectiveness, achieving state-of-the-art FID scores on the high-resolution Image Net benchmark. |
| Researcher Affiliation | Industry | Diederik P. Kingma Google Deep Mind EMAIL Ruiqi Gao Google Deep Mind EMAIL |
| Pseudocode | No | The paper describes mathematical derivations and experimental setups but does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to open-source code for the methodology it describes, nor does it explicitly state that its code is being released. |
| Open Datasets | Yes | Samples generated from our VDM++ diffusion models trained on the Image Net dataset; see Section 5 for details and Appendix M for more samples. |
| Dataset Splits | Yes | We trained the model for 700k iterations and reported the performance of the checkpoint giving the best FID score (checkpoints were saved and evaluated on every 20k iterations). |
| Hardware Specification | Yes | We employed 128 TPU-v4 chips with a batch size of 4096 (32 per chip). |
| Software Dependencies | No | The paper mentions that 'The model is optimized by Adam [Kingma and Ba, 2014]' but does not provide specific version numbers for Adam or any other software dependencies. |
| Experiment Setup | Yes | The model was trained with learning rate 1e-4, exponential moving average of 50 million images and learning rate warmup of 10 million images, which mainly follows the configuration of Karras et al. [2022]. |