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..
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
Authors: TaeHo Yoon, Kibeom Myoung, Keon Lee, Jaewoong Cho, Albert No, Ernest Ryu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that censoring can be accomplished with extreme human feedback efficiency and that labels generated with a mere few minutes of human feedback are sufficient. We conduct experiments within multiple setups demonstrating how minimal human feedback enables removal of target concepts. |
| Researcher Affiliation | Collaboration | 1Department of Mathematical Science, Seoul National University 2Interdisciplinary Program in Artificial Intelligence, Seoul National University 3Department of Electronic and Electrical Engineering, Hongik University 4KRAFTON |
| Pseudocode | Yes | Algorithm 1 Reward model ensemble, Algorithm 2 Imitation learning of reward model |
| Open Source Code | Yes | Code available at: https://github.com/tetrzim/diffusion-human-feedback. |
| Open Datasets | Yes | MNIST [11], LSUN [47], Image Net [10], Image Net1k [10] |
| Dataset Splits | No | The paper does not explicitly state specific train/validation/test splits with percentages or counts for the datasets used in its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud instance specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like ResNet18 architecture and torchvision.models DEFAULTS, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train the diffusion model for 100,000 iterations using the Adam W [27] optimizer with β1 = 0.9 and β2 = 0.999, using learning rate 10 4, EMA with rate 0.9999 and batch size 256. We use 1,000 DDPM steps. |